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Event-based models focus on modelling of peak runoff from rainfall data. Conceptual models indicate simplified models that provide reasonably accurate answers despite their crude nature. Rainfall-runoff models are used to transform a rainfall event into a runoff event. This paper focuses on reviewing computational simulation of rainfall-runoff processes over a catchment. Lumped conceptual, event-based rainfall-runoff models have remained the dominant practice for design flood estimation in Australia for many years due to their simplicity, flexibility, and accuracy under certain conditions. Attempts to establish regionalization methods for prediction of design flood hydrographs in ungauged catchments have seen little success. Therefore, as well as reviewing key rainfall-runoff model components for design flood estimation with a special focus on event-based conceptual models, this paper covers the aspects of regionalization to promote their applications to ungauged catchments. [ABSTRACT FROM AUTHOR]

Hydrological models are traditionally calibrated against observed discharge. However, for a given model, similar performance for discharge simulation can be achieved through a variety of parameter combinations, some of which produce unrealistic simulations of non‐discharge variables. Thus, considering non‐discharge variables in calibration can help to reduce equifinality and give more realistic simulations. Few studies considered non‐discharge variables in calibration and evaluation and across a large sample of catchments. In this study, we calibrate the lumped SIMHYD model with several combinations of different hydrological variables such as discharge, actual evapotranspiration, soil wetness, and total water storage. The various calibration scenarios are conducted across 550 catchments in Australia spanning arid, tropical and temperate regions. We show that the performance of the discharge‐calibrated model for actual evapotranspiration, soil wetness, and total water storage is strongly related to the co‐seasonality of discharge and the respective variable. Considering actual evapotranspiration, soil wetness, or total water storage in addition to discharge in calibration improves the simulation of the added variable on average by 53%, 58%, and 41%, respectively, which comes at the expense of decrease in discharge performance by 5%. Cross‐benefits, that is, the improved simulation of a variable by considering another variable in calibration, mainly occurs between actual evapotranspiration and total water storage and are most pronounced in humid catchments with highly seasonal soil wetness. We also find that when using all four variables in model calibration, the trade‐off in model performance between discharge and the other variables gets more pronounced with increasing catchment wetness. Key Points: Using four different hydrological variables, multivariate calibration and evaluation were undertaken in 550 Australian catchmentsBenefit of multivariate calibration across non‐discharge variables is higher in humid than in (semi‐) arid climatesPronounced evaluation cross‐benefits between evapotranspiration and total water storage in temperate climates with seasonal soil wetness [ABSTRACT FROM AUTHOR]

Understanding the origins of errors between model predictions and catchment observations is a critical element in hydrologic model calibration and uncertainty estimation. Difficulties arise because there are a variety of error sources but only one measure of the total residual error between model predictions and catchment observations. One promising approach is to collect extra information a priori to characterize the data error before calibration. We implement here a new model calibration strategy for an ecohydrological model, using the satellite metadata information as a means to inform the model priors, to decompose data error from total residual error. This approach, referred to as Bayesian ecohydrological error model (BEEM), is first examined in a synthetic setting to establish its validity, and then applied to three real catchments across Australia. Results show that (a) BEEM is valid in a synthetic setting, as it can perfectly ascertain the true underlying error; (b) in real catchments the model error is reduced when utilizing the observation error variance as added error contributing to total error variance, while the magnitude of total residual error is more robust when utilizing metadata about the data quality proportionality as the basis for assigning total error variance; (c) BEEM improves model calibration by estimating the model error appropriately and estimating the uncertainty interval more precisely. Overall, our work demonstrates a new approach to collect prior error information in satellite metadata and reveals the potential for fully utilizing metadata about error sources in uncertainty estimation. Plain Language Summary: Ecohydrological models use climate data to make predictions about streamflow and vegetation variables, such as leaf area index (LAI). However, these models have errors that affect their accuracy. Generally, three sources of error may impair model prediction and uncertainty analysis: (a) data error; (b) parameter error; and (c) model structure error. These errors are difficult to identify, as they are only observed through the mismatch between the model output and observations. One solution is to collect independent information about the data error to help identify different sources error and to undertake uncertainty analysis. If we can estimate independently that the observations have a certain amount of error (e.g., due to the sensing equipment accuracy) then we can estimate what the remaining error is via a statistical model. Following this logic, this study collected prior info of LAI error based on satellite metadata to improve our estimates of the total model error. We call this approach BEEM (Bayesian Ecohydrological Error Model), and this study is focused on how BEEM improves the uncertainty analysis of a model. Results show that BEEM gives more accurate model prediction intervals in real case studies. Key Points: A Bayesian ecohydrological error model (BEEM) is proposed to separate data error from total residual errorThe BEEM uses error information from satellite LAI metadata and reveals the potential of utilizing prior information in uncertainty estimationThe BEEM works well in both synthetic case and real catchments [ABSTRACT FROM AUTHOR]

We present an integrated framework that leverages multiple weather radar calibration and monitoring techniques to provide real-time diagnostics on reflectivity calibration, antenna pointing, and dual-polarization moments. This framework uses a volume-matching technique to track the absolute calibration of radar reflectivity with respect to the Global Precipitation Measurement (GPM) spaceborne radar, the relative calibration adjustment (RCA) technique to track relative changes in the radar calibration constant, the solar calibration technique to track daily change in solar power and antenna pointing error, and techniques that track properties of light-rain medium to monitor the differential reflectivity and dual-polarization moments. This framework allows for an evaluation of various calibration and monitoring techniques. For example, we found that a change in the RCA is highly correlated to a change in absolute calibration, with respect to GPM, if a change in antenna pointing can first be ruled out. It is currently monitoring 67+ radars from the Australian radar network. Because of the diverse and evolving nature of the Australian radar network, flexibility and modularity are at the core of the calibration framework. The framework can tailor its diagnostics to the specific characteristics of a radar (band, beamwidth, etc.). Because of its modularity, it can be expanded with new techniques to provide additional diagnostics (e.g., monitoring of radar sensitivity). The results are presented in an interactive dashboard at different level of details for a wide and diverse audience (radar engineers, researchers, forecasters, and management), and it is operational at the Australian Bureau of Meteorology. Significance Statement: Weather radars, like all instruments, require maintenance and upgrades. Rainfall measurements are highly variable and sensitive to change, and this can lead to inconsistencies within a radar network. Calibration is the process to counteract those inconsistencies. Any calibration requires a fixed standard to which the changed/upgraded radar can be compared. The SCAR calibration framework presented herein makes use of several standards to retrieve a full set of diagnostics about the radar data. We apply these techniques over the entire Australian weather radar network and demonstrate that, by using this integrated approach, absolute calibration can be achieved to within 1 dBZ of reflectivity, antenna pointing can be monitored within 0.1°, and the various measurements of the radars can be quality controlled. [ABSTRACT FROM AUTHOR]

Systematic measurement of pasture biomass (kg DM/ha) is crucial for optimising pasture utilisation and increasing dairy farm profitability. On-farm pasture monitoring can be conducted using various sensors, but calibrations are necessary to convert the measured variable into pasture biomass. In this study, we conducted three experiments in New South Wales (Australia) to evaluate the use of the rising plate meter (RPM), pasture reader (PR), unmanned aerial vehicles (UAV) and satellites as pasture monitoring tools. We tested various calibration methods that can improve the accuracy of the estimations and be implemented more easily on-farm. The results indicate that UAV and satellite-derived reflectance indices (e.g., Normalised Difference Vegetation Index) can be indirectly calibrated with height measurements obtained from an RPM or PR. Height measurements can be then converted into pasture biomass ideally by conducting site-specific sporadic calibrations cuts. For satellites, using the average of the entire paddock, root mean square error (RMSE) = 226 kg DM/ha for kikuyu (Pennisetum clandestinum Hochst. ex Chiov) and 347 kg DM/ha for ryegrass (Lolium multiflorum L.) is as effective as but easier than matching NDVI pixels with height measurement using a Global Navigation Satellite System (RMSE = 227 kg DM/ha for kikuyu and 406 kg DM/ha for ryegrass). For situations where no satellite images are available for the same date, the average of all images available within a range of up to four days from the day ground measurements were taken could be used (RMSE = 225 kg DM/ha for kikuyu and 402 kg DM/ha for ryegrass). These methodologies aim to develop more practical and easier-to-implement calibrations to improve the accuracy of the predictive models in commercial farms. However, more research is still needed to test these hypotheses under extended periods, locations, and pasture species. [ABSTRACT FROM AUTHOR]

Aims: Predicting progression to kidney failure for patients with chronic kidney disease is essential for patient and clinicians' management decisions, patient prognosis, and service planning. The Tangri et al Kidney Failure Risk Equation (KFRE) was developed to predict the outcome of kidney failure. The KFRE has not been independently validated in an Australian Cohort. Methods: Using data linkage of the Tasmanian Chronic Kidney Disease study (CKD. TASlink) and the Australia and New Zealand Dialysis and Transplant Registry (ANZDATA), we externally validated the KFRE. We validated the 4, 6, and 8-variable KFRE at both 2 and 5 years. We assessed model fit (goodness of fit), discrimination (Harell's C statistic), and calibration (observed vs predicted survival). Results: There were 18 170 in the cohort with 12 861 participants with 2 years and 8182 with 5 years outcomes. Of these 2607 people died and 285 progressed to kidney replacement therapy. The KFRE has excellent discrimination with C statistics of 0.96-0.98 at 2 years and 0.95-0.96 at 5 years. The calibration was adequate with well-performing Brier scores (0.004-0.01 at 2 years, 0.01-0.03 at 5 years) however the calibration curves, whilst adequate, indicate that predicted outcomes are systematically worse than observed. Conclusion: This external validation study demonstrates the KFRE performs well in an Australian population and can be used by clinicians and service planners for individualised risk prediction. [ABSTRACT FROM AUTHOR]

Aims: The Australian estimated post‐transplant survival (EPTS‐AU) prediction score was developed by re‐fitting the United States of America EPTS, without diabetes, to the Australian and New Zealand kidney transplant population over 2002–2013. The EPTS‐AU score incorporates age, previous transplantation and time on dialysis. Diabetes was excluded from the score, as this was not previously recorded in the Australian allocation system. In May 2021, the EPTS‐AU prediction score was incorporated into the Australian kidney allocation algorithm to optimize utility for recipients (maximized benefit). We aimed to temporally validate the EPTS‐AU prediction score to ensure it can be used for this purpose. Methods: Using the Australia and New Zealand Dialysis and Transplant (ANZDATA) Registry, we included adult recipients of deceased donor kidney‐only transplants between 2014 and 2021. We constructed Cox models for patient survival. We assessed validation using measures of model fit (Akaike information criterion and misspecification), discrimination (Harrell's C statistic and Kaplan–Meier curves), and calibration (observed vs. predicted survival). Results: Six thousand four hundred and two recipients were included in the analysis. The EPTS‐AU had moderate discrimination with a C statistic of 0.69 (95% CI 0.67, 0.71), and clear delineation between Kaplan–Meier's survival curves of EPTS‐AU. The EPTS was well calibrated with the predicted survivals equating with the observed survival outcomes for all prognostic groups. Conclusions: The EPTS‐AU performs reasonably well in choosing between recipients (discrimination) and to predict a recipient's survival (calibration). Reassuringly, the score is functioning as intended to predict post‐transplant survival for recipients as part of the national allocation algorithm. [ABSTRACT FROM AUTHOR]

Satellite remote sensing provides a unique opportunity for calibrating land surface models due to their direct measurements of various hydrological variables as well as extensive spatial and temporal coverage. This study aims to apply terrestrial water storage (TWS) estimated from the gravity recovery and climate experiment (GRACE) mission as well as soil moisture products from advanced microwave scanning radiometer–earth observing system (AMSR-E) to calibrate a land surface model using multi-objective evolutionary algorithms. For this purpose, the non-dominated sorting genetic algorithm (NSGA) is used to improve the model's parameters. The calibration is carried out for the period of two years 2003 and 2010 (calibration period) in Australia, and the impact is further monitored over 2011 (forecasting period). A new combined objective function based on the observations' uncertainty is developed to efficiently improve the model parameters for a consistent and reliable forecasting skill. According to the evaluation of the results against independent measurements, it is found that the calibrated model parameters lead to better model simulations both in the calibration and forecasting period. [ABSTRACT FROM AUTHOR]

A cost‐effective way of undertaking comprehensive, continental‐scale, assessments of ecological condition is needed to support large‐scale conservation planning, monitoring, reporting, and decision‐making. Currently, cross‐jurisdictional inconsistency in assessment methods limits the capacity to scale‐up monitoring. Here we present a novel way to build a coherent continent‐wide site‐level ecological condition dataset, using cross‐calibration methods to integrate assessments from many observers. We focus on the use of condition assessments from individual expert observers, a currently untapped resource. Our approach has two components: (1) a simple online tool that captures expert assessments at specific locations; (2) a process of calibrating and rescaling disparate expert evaluations that can be applied to the data to provide a consistent dataset for use in conservation assessments. We describe a pilot study, involving 28 experts, who contributed 314 individual site condition assessments across a wide range of ecosystems and regions throughout continental Australia. A correction factor for each expert was used to rescale the contributed site condition assessment scores, based on a set of 77 photographic images, each scored for their condition by multiple experts, using a linear mixed model. Our approach shows strong promise for delivering the volumes of data required to develop continental‐scale reference libraries of site condition assessments. Although developed from expert elicitation, the approach could also be used to harmonize the collation of existing condition datasets. The process we demonstrate can also facilitate online citizen scientists to make site condition assessments that can be cross‐calibrated using contributed images. [ABSTRACT FROM AUTHOR]

Purpose: To assess the number of radiotherapy kilovoltage (kV) units in service, their clinical utilization, and methodology and equipment used for absorbed dose determination across Australia and New Zealand., Methods: A survey was sent to 61 Australian and New Zealand radiotherapy providers in the second half of 2023., Results: Fifty-seven responses were received, with 43 departments having kV units and providing beam quality data for 185 therapeutic kV beams 20-300 kVp. Percentage depth dose curves were compared between five clinical beams with 100 kVp and 2.13-6.28 mm Aluminum half value layers (HVLs), demonstrating large differences that can occur between beams with the same kVp. Eighteen departments provided clinical utilization data for their kV units, with a total of 4458 treatment courses and their corresponding kVp reported. All departments complied with national and international recommendations with respect to the equipment used for reference dosimetry of kV beams; 77% of ionization chambers used for absorbed dose determination were of Farmer-type, with the remaining 23% being plane parallel soft x-ray chambers. Methods of derivation of air-kerma calibration factors varied, with 73% of respondents using a draft document disseminated by the Australian Primary Standards laboratory, 23% using HVL alone, and 6% using other methods., Conclusions: The results of this survey provide a snapshot of kilovoltage radiation therapy use and the number of kV units across Australia and New Zealand. This data can be used as a point of reference for future investigations into clinical utilization and reference dosimetry methods across Australia and New Zealand or for comparisons with other countries, facilitating standardization of reference dosimetry practice for kilovoltage units., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Dual energy x-ray absorptiometry (DXA) devices were installed at two Australian abattoirs to predict computed tomography (CT) determined fat % and lean % of lamb carcasses. This study tested three algorithms developed for these devices, termed β1, β2 and β3, and assessed their accuracy and precision in predicting CT composition. Algorithm β3 included the use of a plastic phantom calibration block scanned by both DXA devices to adjust prediction equations, resulting in superior accuracy to the algorithms without phantom calibration (β1 and β2). When compared to the gold-standard CT composition, the bias of the DXA predictions was lowest when using algorithm β3 at the two sites (-1.17%, -0.49% for fat %, 0.11%, -0.37% for lean %). The difference of DXA composition predictions between sites was lowest when using algorithm β3, which demonstrated between site differences of 0.59 CT fat %, and 0.46 CT lean%. In contrast, algorithm β1 and β2 produced differences of 23.7% and 30.8% for CT fat, and 17.3% and 21.9% for CT lean between the two DXA devices. There was a small difference of 0.78% between the fat predictions of the first DXA image compared to the second DXA image for each carcass. The precision of predictions improved slightly using algorithm β3. This work demonstrates that the in-line DXA systems can produce comparable results across sites., Competing Interests: Declaration of competing interest There is no conflict of interest involved in this work, (Copyright © 2024. Published by Elsevier Ltd.)

We used SUFI‐2 for the first time to calibrate the phenology module of the APSIM‐wheat model for 10 spring wheat cultivars cultivated in northeast Australia (south‐eastern Queensland). Calibration resulted in an average root mean square error (RMSE) of 5.5 days for developmental stages from stem elongation up to flowering. Projections from 33 climate models under the representative concentration pathway 8.5 were used for simulations at 17 sites. Using adapted sowing times, we simulated significantly shorter crop cycles and grain yield improvements for the period 2036–2065 relative to 1990–2019 for three selected cultivars (Hartog, Scout and Gregory). Photoperiod and vernalisation sensitivities were shown to be the largest and smallest contributors to total uncertainties in the simulated flowering day and grain yield, respectively. Uncertainties in climate models had a relatively minor contribution to the total uncertainties in the simulated values of target traits. This contribution significantly increased when climate change impact on the target traits was quantified. [ABSTRACT FROM AUTHOR]

Understanding the impact of management interventions on the environment over decadal and longer timeframes is urgently required. Longitudinal or large-scale studies with consistent methods are best practice, but more commonly, small datasets with differing methods are used to achieve larger coverage. Changes in methods and interpretation affect our ability to understand data trends through time or across space, so an ability to understand and adjust for such discrepancies between datasets is important for applied ecologists. Calibration or double sampling is the key to unlocking the value from disparate datasets, allowing us to account for the differences between datasets while acknowledging the uncertainties. We use a case study of livestock grazing impacts on riparian vegetation in southeastern Australia to develop a flexible and powerful approach to this problem. Using double sampling, we estimated changes in vegetation attributes over a 12-year period using a pseudo-quantitative visual method as the starting point, and the same technique plus point-intercept survey for the second round. The disparate nature of the datasets produced uncertain estimates of change over time, but accounting for this uncertainty explicitly is precisely the objective and highlights the need to look more closely at this very common problem in environmental management, as well as the potential benefits of the double sampling approach. [ABSTRACT FROM AUTHOR]

Background: Endovascular revascularization (ER) via percutaneous transluminal angioplasty (PTA) and stenting are viable options for revascularization in below-the-knee (BTK) peripheral arterial disease. Two-dimensional angiography has been the standard of practice for estimating vessel size and selecting treatment devices during ER. However, in other vascular territories, intravascular ultrasound (IVUS) offers better visualization of the lumen dimensions.Purpose: To compare angiographic and intravascular ultrasound reference vessel (lumen) measurements in below-the-knee peripheral artery interventions.Methods: Twenty consecutive patients were enrolled in the BTK Calibration study from 2 sites in the United States and Australia. Patients with at least one diseased segment in a native infra-popliteal artery (below-the-knee) and a clinical indication for endovascular therapy (EVT) were included with no limitations with regard to vessel diameter or lesion length. Digital subtraction angiography and IVUS imaging were collected pre- and post-PTA and images were sent to an independent core lab for standardized quantitative analysis of the normal-looking reference vessel dimensions when available. The results were presented as least square means with 95% confidence intervals and a p-value of <0.05 was considered significant.Results: The overall (N = 19) mean reference vessel diameter for QVA was 2.98 ± 1.24 mm vs. 3.47 ± 0.72 mm for IVUS (mean difference was -0.50 mm, (95% CI: -0.80, -0.20; p = 0.14). As expected, in the proximal segments (N = 12), the mean reference vessel diameters were larger: for QVA, it was 3.17 ± 1.34 mm vs. 3.55 ± 0.76 mm in IVUS, (mean difference was -0.38 mm, (95% CI: -0.79, 0.03; p = 0.40); while in the distal segments (N = 7), mean reference vessel diameters were smaller: for QVA, it was 2.64 ± 1.06 mm vs. 3.33 ± 0.67 mm in IVUS, (mean difference was -0.69 mm, (95% CI: -1.04, 0.34; p = 0.17). We observed a greater degree of acute gain in cases where the treatment balloon size correlated with the IVUS measured reference size.Conclusion: Angiography underestimates infrapopliteal reference vessel lumen size even when quantitatively assessed. Adjunctive IVUS imaging use in guiding BTK procedures could help ensure adequate sizing and possibly impact immediate post-procedure indices. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop calibration models against rib eye traits and independently validate the precision, accuracy, and repeatability of the Frontmatec Q-FOM™ Beef grading camera in Australian carcasses. This study compiled 12 different research datasets acquired from commercial processing facilities and were comprised of a diverse range of carcass phenotypes, graded by industry identified expert Meat Standards Australia (MSA) graders and sampled for chemical intramuscular fat (IMF%). Calibration performance was maintained when the device was independently validated. For continuous traits, the Q-FOM™ demonstrated precise (root mean squared error of prediction, RMSEP) and accurate (coefficient of determination, R 2 ) prediction of eye muscle area (EMA) (R 2  = 0.89, RMSEP = 4.3 cm 2 , slope = 0.96, bias = 0.7), MSA marbling (R 2  = 0.95, RMSEP = 47.2, slope = 0.98, bias = -12.8) and chemical IMF% (R 2  = 0.94, RMSEP = 1.56%, slope = 0.96, bias = 0.64). For categorical traits, the Q-FOM™ predicted 61%, 64.3% and 60.8% of AUS-MEAT marbling, meat colour and fat colour scores equivalent, and 95% within ±1 classes of expert grader scores. The Q-FOM™ also demonstrated very high repeatability and reproducibility across all traits., Competing Interests: Declaration of competing interest None., (Copyright © 2024. Published by Elsevier Ltd.)

A prototype, on-line Dual Energy X-ray Absorptiometer (DXA) has shown high precision of the prediction of carcass composition for the purpose of improved sheep meat grading in the Australian lamb supply chain, albeit with small inaccuracies over time. These inaccuracies were present across hours, and more significantly across days, which were unacceptable for any accreditation of this device as an objective carcass measurement tool in Australia. This inaccuracy demanded the creation of a novel image-processing algorithm for the prototype DXA. This DXA was tested for repeatability of predictions of lamb carcass composition over minutes, hours, and days, using two developed image processing algorithms. There was high immediate repeatability for both algorithms when predicting lean muscle % in 40 lamb carcasses, with a maximum CV of 0.65% over five repeated scans. There was a decrease in the CV of the prediction of lean muscle % of 30 lambs scanned three times over a 48-h period from 5.93 to 1.19% when the superior algorithm was used. The inaccuracies of lean muscle % predictions were associated with increases in the unattenuated space pixel values in DXA images. Improvements of the current algorithm are required to demonstrate repeatability over time for the purpose of accreditation within the Australian sheep meat industry, and for possible expansion of this technology into international supply chains., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Disease surveillance and response are critical components of epidemic preparedness. The disease response, in most cases, is a set of reactive measures that follow the outcomes of the disease surveillance. Hence, timely surveillance is a prerequisite for an effective response. Methodology/principal findings: We apply epidemiological soundness criteria in combination with the Latent Influence Point Process and time-to-event models to construct a disease spread network. The network implicitly quantifies the fertility (whether a case leads to secondary cases) and reproduction (number of secondary cases per infectious case) of the cases as well as the size and generations (of the infection chain) of the outbreaks. We test our approach by applying it to historic dengue case data from Australia. Using the data, we empirically confirm that high morbidity relates positively with delay in disease response. Moreover, we identify what constitutes timely surveillance by applying various thresholds of disease response delay to the network and report their impact on case fertility, reproduction, number of generations and ultimately, outbreak size. We observe that enforcing a response delay threshold of 5 days leads to a large average reduction across all parameters (occurrence 87%, reproduction 83%, outbreak size 80% and outbreak generations 47%), whereas extending the threshold to 10 days, in comparison, significantly limits the effectiveness of the response actions. Lastly, we identify the components of the disease surveillance system that can be calibrated to achieve the identified thresholds. Conclusion: We identify practically achievable, timely surveillance thresholds (on temporal scale) that lead to an effective response and identify how they can be satisfied. Our approach can be utilized to provide guidelines on spatially and demographically targeted resource allocation for public awareness campaigns as well as to improve diagnostic abilities and turn-around times for the doctors and laboratories involved. [ABSTRACT FROM AUTHOR]

Background: Mortality modelling in the critical care paradigm traditionally uses logistic regression, despite the availability of estimators commonly used in alternate disciplines. Little attention has been paid to covariate endogeneity and the status of non-randomized treatment assignment. Using a large registry database, various binary outcome modelling strategies and methods to account for covariate endogeneity were explored.Methods: Patient mortality data was sourced from the Australian & New Zealand Intensive Society Adult Patient Database for 2016. Hospital mortality was modelled using logistic, probit and linear probability (LPM) models with intensive care (ICU) providers as fixed (FE) and random (RE) effects. Model comparison entailed indices of discrimination and calibration, information criteria (AIC and BIC) and binned residual analysis. Suspect covariate and ventilation treatment assignment endogeneity was identified by correlation between predictor variable and hospital mortality error terms, using the Stata™ "eprobit" estimator. Marginal effects were used to demonstrate effect estimate differences between probit and "eprobit" models.Results: The cohort comprised 92,693 patients from 124 intensive care units (ICU) in calendar year 2016. Patients mean age was 61.8 (SD 17.5) years, 41.6% were female and APACHE III severity of illness score 54.5(25.6); 43.7% were ventilated. Of the models considered in predicting hospital mortality, logistic regression (with or without ICU FE) and RE logistic regression dominated, more so the latter using information criteria indices. The LPM suffered from many predictions outside the unit [0,1] interval and both poor discrimination and calibration. Error terms of hospital length of stay, an independent risk of death score and ventilation status were correlated with the mortality error term. Marked differences in the ventilation mortality marginal effect was demonstrated between the probit and the "eprobit" models which were scenario dependent. Endogeneity was not demonstrated for the APACHE III score.Conclusions: Logistic regression accounting for provider effects was the preferred estimator for hospital mortality modelling. Endogeneity of covariates and treatment variables may be identified using appropriate modelling, but failure to do so yields problematic effect estimates. [ABSTRACT FROM AUTHOR]

• Physics-based method estimating all components of surface solar irradiance (SSI). • Calibration removing requirement of any ancillary data during operation. • Time series based radiative transfer model to simultaneously retrieve properties of clouds, aerosols and surface. • Comprehensive validation showing consistently low bias and relatively small RMSE for all components. • For examples, <4 W/m2 bias for all components and time scales, and 58 W/m2 GHI hourly RMSE. Surface Solar Irradiance (SSI) is required for solar energy planning and adoption, and is a fundamental parameter in modelling weather, climate, ecosystem and agricultural activities. Herein a time series based radiative transfer model was developed to simultaneously retrieve properties of clouds, aerosols and surface albedo, which were in turn used to calculate the components of SSI: i.e., global, direct and diffuse irradiance. The calculated results were calibrated across the Australian continent against in-situ measurements to account for minor factors (e.g., water vapor) not considered by the physics-based method. Detailed validation of the SSI components was performed against three years of in-situ measurements at 11 sites across Australia, at a range of time scales (i.e., instantaneous, hourly, daily and monthly) and under both all-sky and cloudy-sky conditions. The main advantage of the present method is the reliable separation of the direct and diffuse components with consistently low biases (~4 W/m2) at all four time scales, while still maintaining relatively low RMSE (root-mean-square-error) and MAE (mean-absolute-error). Once calibration has been performed the model does not require any ancillary data when implemented operationally: that is the model only requires geostationary satellite data. This model may be implemented across the globe using widely available next generation geostationary satellite data with a handful of ground-data for calibration. [ABSTRACT FROM AUTHOR]

The fourth and fifth Soil Moisture Active Passive Experiments (SMAPEx-4 and -5) were conducted at the beginning of the SMAP operational phase, May and September 2015, to: 1) evaluate the SMAP microwave observations and derived soil moisture (SM) products and 2) intercompare with the Soil Moisture and Ocean Salinity (SMOS) and Aquarius missions over the Murrumbidgee River Catchment in the southeast of Australia. Airborne radar and radiometer observations at the same microwave frequencies as SMAP were collected over SMAP footprints/grids concurrent with its overpass. In addition, intensive ground sampling of SM, vegetation water content, and surface roughness was carried out, primarily for validation of airborne SM retrieval over six ~ 3 km × 3 km focus areas. In this study, the SMAPEx-4 and -5 data sets were used as independent reference for extensively evaluating the brightness temperature and SM products of SMAP, and intercompared with SMOS and Aquarius under a wide range of SM and vegetation conditions. Importantly, this is the only extensive airborne field campaign that collected data while the SMAP radar was still operational. The SMAP radar, radiometer, and derived SM showed a high agreement with the SMAPEx-4 and -5 data set, with a root-mean-squared error (RMSE) of ~3 K for radiometer brightness temperature, and an RMSE of ~ 0.05 m3/m3 for the radiometer-only SM product. The SMAP radar backscatter had an RMSE of 3.4 dB, while the retrieved SM had an RMSE of 0.11 m3/m3 when compared with the SMAPEx-4 data set. [ABSTRACT FROM AUTHOR]

Field measurement and modelling of soil erosion provides insights into landscape systems as well as the potential for enhanced landscape management. There are a number of field and numerical methods by which soil erosion and deposition can be quantified. Here we examine the capability of the SIBERIA landscape evolution model to quantify short‐term erosion and deposition on a well‐managed cattle grazing landscape on the east coast of Australia. The model is calibrated by two methods (1) a geomorphological approach using a site digital elevation model (DEM) and soil data and (2) a laboratory‐scale flume. The two calibration processes resulted in similar model input parameters and estimated erosion rates of 3.1 t ha−1 year−1 and 4.4 t ha−1 year−1, respectively. These were found to closely match erosion rates estimated using the environmental tracer 137Cs (2.7–4.8 t ha−1 year−1). However, erosion and deposition estimated at individual points along the hillslope was not well correlated with 137Cs at the same position due to the temporal averaging of the model and microtopography. Sensitivity analysis showed the model was more sensitive to parameterisation than sub‐DEM‐scale topography. This places confidence in the model's ability to estimate erosion and deposition across an entire hillslope and catchment on decadal time scales. We also highlight the robustness and flexibility of the calibration methods. [ABSTRACT FROM AUTHOR]

Accurate and reliable seasonal climate forecasts are frequently sought by climate‐sensitive sectors to support decision‐making under climate variability and change. Temperature trend is discernible globally over the past decades, but seasonal forecasts produced by a global climate model (GCM) generally underestimate such trend. Current statistical methods used for calibrating seasonal climate forecasts mostly do not explicitly account for climate trends. Consequently, the calibrated forecasts also fail to capture the observed trend. Solving this problem can enhance user confidence in seasonal climate forecasts. In this study, we extend the capability of the Bayesian joint probability (BJP) modelling approach for statistical calibration of seasonal climate forecasts. A trend component is introduced into the BJP algorithm for embedding the observed trend into calibrated ensemble forecasts. We apply the new model (named BJP‐t) to three test stations in Australia. Seasonal forecasts of daily maximum temperatures from the SEAS5 model, operated by the European Centre for Medium‐Range Weather Forecasts (ECMWF), are calibrated and evaluated. The BJP‐t calibrated ensemble forecasts can reproduce the observed trend, when the raw ensemble forecasts and the BJP calibrated ensemble forecasts both fail to do so. The BJP‐t calibration leads to more skilful, more reliable and sharper forecasts than the BJP calibration. [ABSTRACT FROM AUTHOR]

Background: In the University setting, striving for consistency and reliability of assessment evaluation is essential to reducing the impact of marking variations. Marking processes such as consensus calibration have the potential to reduce issues which arise from the influence of markers professional knowledge and experience, as well as fixed and acquired marking habits. Furthermore, the influence of marker feedback which supports learning development is associated with the feedback literacy of both the teacher and the student. A gap is currently present in the literature as these practices are not discussed together., Objectives: To explore how nursing academics perceive and understand calibration practices and associated feedback literacy., Design: Theoretical underpinnings in participatory and person-centred research methodology supported the critical ontological perspective of this study where the intent of the research was to explore the reality that exists within the context where the research was conducted., Setting: A single School of Nursing in an Australian University with six campuses spanning metropolitan, regional and rural sites., Participants: Nursing academics and casual tutors with various levels of experience in assessment marking and feedback., Methods: Semi-structured group interviews that were analysed using reflexive thematic analysis., Results: Four overarching themes were identified; rubrics, calibration, feedback and justice., Conclusion: Calibration improves staff cohesion, fosters better practices and consistency, and permits nuanced interpretation of assessments while maintaining uniformity. Enhanced feedback literacy that integrates principles of equity, justice, and learner-centeredness is required. Fundamentally calibration guides educators toward holistic approaches that foster consistency, equity, and thorough feedback practices., Competing Interests: Declaration of competing interest All authors work or have worked in the School of Nursing where data was collected. No authors were involved in data collection or had any knowledge of the participants. An independent facilitator collected all consent forms and conducted all group interviews. No authors have any conflicts of interest related to this publication., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Rationale and objectives: In epidemiological research, it is essential to account for the confounding effects of factors such as age, stage, and comorbidity for accurate prediction of cancer outcomes. There are several internationally developed and commonly used comorbidity indices. However, none are regarded as the gold‐standard method. This study will assess and compare the predictive validity of established indices for use in a South Australian (SA) colorectal cancer (CRC) population against a local index. Furthermore, the prognostic influence of comorbidity on survival is investigated. Methods: A population‐based study of patients diagnosed with CRC from 2003 to 2012 and linked to in‐hospital data to retrieve comorbidity information was conducted. The predictive performance of established indices, Charlson comorbidity index (CCI), National Cancer Institute comorbidity index (NCI), Elixhauser comorbidity index (ECI), and C3 index was evaluated using the Fine and Gray competing risk regression and reported using measures of calibration and discrimination, area under the curve (AUC), and Brier score. Furthermore, to identify the optimal index, a local CRC comorbidity index (CRCCI) was also developed and its performance compared with the established indices. Results: Comorbidity models adjusted for age, sex, and stage showed that all indices were good predictors of mortality as measured by the AUC (CCI: 0.738, NCI: 0.742, ECI: 0.733, C3: 0.739). CRCCI had similar mortality prediction as established indices (CRCCI: 0.747). There was a significant increase in cumulative risk of noncancer and CRC‐specific mortality with increase in comorbidity scores. The two most prevalent comorbidities were hypertension and diabetes. Conclusions: The existing indices are still valid for adjusting for comorbidity and accurately predicting mortality in an SA CRC population. Internationally developed indices are preferred when policymakers and researchers wish to compare local study results with those of studies (national and international) that have used these indices. Comorbidity is a predictor of mortality and should be considered when assessing CRC survival. [ABSTRACT FROM AUTHOR]

Context. Near-infrared reflectance spectroscopy (NIRS) is a tool that permits rapid and inexpensive prediction of the nutritional characteristics of forages consumed by ruminants. Aim. Our aim was to investigate the feasibility of developing a NIRS calibration to predict the nutritional value of the majority of grasses, legumes and forbs that are utilised for sheep and cattle production in southern Australia. Methods.More than 100 annual and perennial forage species were grown in replicated plots at two locations over a period of 3 years. Biomass was sampled every 3-6 weeks, dried, ground and scanned with a desktop NIRS machine (n = 4385). One-quarter of these samples were subjected to laboratory analysis for calibration development or validation. Key results. Despite the large variation in the taxonomy and maturity of the plants when sampled, we successfully developed broad calibrations that predicted key nutritional traits. We achieved excellent predictions for crude protein, with a ratio of standard error of performance : standard deviation (RPD) of 5.3, and standard error of cross validation (SECV) of 1.06%. Predictions of neutral detergent fibre were also excellent (RPD 4.3, SECV. 3.5%). For pepsin-cellulase DM digestibility and acid detergent fibre, predictions were very good (RPD 3.7, SECV 2.6% and RPD 3.9, SECV 2.1%). Predictions for organic matter were less reliable (RPD 2.2). We achieved very promising predictions of methane production during batch culture fermentation (RPD 3.1, SECV 3.5 mL/gDM). Predictions of ammonia and total volatile fatty acid concentrations in the post-fermentation substrate were poor. Conclusions. We found that the broad calibrations predicted the nutritional traits of annual grasses, annual legumes and forb species with greater accuracy than perennial grasses or legumes. This could be associated with the accuracy of the wet chemistry methods. As a general rule, separating taxonomically similar species into groups before the development of calibrations, did not lead to more accurate predictions. Implications. If more spatial and temporal diversity can be built in without a large reduction in accuracy, these broad NIRS calibrations represent a valuable tool for Australian researchers, feed testing agents and livestock producers, as they encompass nearly all of the species that appear in monocultures or mixed swards. [ABSTRACT FROM AUTHOR]

The Australian water balance model (AWBM) is automatically calibrated to 204 data sets from the main stream-gauging network in Australia. This daily rainfall-runoff model calculates surface runoff and baseflow as separate components of runoff, and the curve number (CN) is calibrated to the surface runoff component in each data set. The catchment areas ranging from 52 to 1,970 km2 contain mixes of soils and vegetation, but the calibrated CN can be used in the catchment areas and surrounding regions. There was a distinct relationship between the median calibrated CN for average antecedent rainfall (CNII) value of each group of catchments and the mid latitude of the group with an increase of one in the median CNII for each one degree increase in latitude. The method has potential for providing a very large amount of surface runoff data for calibrating the CN from the 3,000 or so stream-gauging stations in Australia. [ABSTRACT FROM AUTHOR]

Background: Self-reported physical activity is inaccurate, yet few investigators attempt to adjust for measurement error when estimating risks for health outcomes. We estimated what the association between self-reported physical activity and colorectal cancer risk would be if physical activity had been assessed using accelerometry instead.Methods: We conducted a validation study in which 235 Australian adults completed a telephone-administered International Physical Activity Questionnaire (IPAQ), and wore an accelerometer (Actigraph GT3X+) for 7 days. Using accelerometer-assessed physical activity as the criterion measure, we calculated validity coefficients and attenuation factors using a structural equation model adjusted for age, sex, education and body mass index. We then used a regression calibration approach to apply the attenuation factors to data from the Melbourne Collaborative Cohort Study (MCCS) to compute bias-adjusted hazard ratios (HR) and 95% confidence intervals (CI).Results: Average daily minutes of physical activity from the short form of the International Physical Activity Questionnaire (IPAQ-short) were substantially higher than accelerometer-measured duration (55 versus 32 min). The validity coefficient (0.32; 95% CI: 0.20, 0.43) and attenuation factor (0.20; 95% CI: 0.12, 0.28) were low. The HRs for colorectal cancer risk for high (75th percentile; 411 min/week) versus low (25th percentile; 62 min/week) levels of self-reported physical activity were 0.95 (95% CI: 0.87, 1.05) before and 0.78 (95% CI: 0.47, 1.28) after bias adjustment.Conclusions: Over-estimation of physical activity by the IPAQ-short substantially attenuates the association between physical activity and colorectal cancer risk, suggesting that the protective effect of physical activity has been previously underestimated. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the rate of compliance of air kerma strength (AKS) measurements of iodine-125 (I-125) seeds with international recommendations by departments in Australia and determine the potential impact of noncompliance. To achieve this aim, we present an intercomparison of AKS measurements for a single I-125 seed performed by 11 radiotherapy departments in Australia. Measurements were performed at two sites, with each participating department traveling to one of the two host sites and measuring the AKS using their own equipment and local protocols. Each of the AKS measurements was compared with each other and the manufacturer-certified AKS. Nine of the 11 participating departments measured AKS fell within ±3% of the manufacturer's calibration certificate value, whereas all participating departments measured AKS within ±5% of the manufacturer's calibration certificate value. The total spread of the measured AKS among the 11 departments was 7.7%. Only two of the 11 participating departments complied with international recommendations and had their well chamber calibrated within the last 2 years. In addition, 2 of the 11 departments used a well chamber calibrated that was calibrated with a different seed model used during the intercomparison, whereas 4 of the 11 departments calibrated their well chamber "in-house" using a factory-calibrated seed provided by the seed manufacturer. A significant variation in the methods used and frequency of calibration of well chambers were observed among the participating departments. The results of this study support the international recommendations on frequency and methodology of well chamber calibration. Failure to follow these recommendations significantly increases the uncertainty in AKS measurement of I-125 seeds. [ABSTRACT FROM AUTHOR]

Desert ants navigate by using two chief strategies: path integration, keeping track of the straight‐line distance and direction to the starting point as they travel, and landmark guidance, orientation based on the visual panorama. Both Cataglyphis ants in North Africa and Melophorus bagoti in Central Australia are known to adjust their vectors derived from path integration to compensate for mismatches between their outbound direction of travel and (the reverse of) the inbound direction of travel that takes them home, a process known as vector calibration. We created mismatches of 90° between the outbound vector and the homebound direction by displacing ants from a feeder before their homebound run. We examined temporal factors in vector calibration by varying the delay (0, 1 or 3 hr) between the outbound run to the feeder and the homebound run from the displacement site. According to the temporal weighting rule, such a delay should decrease the weight given to the vector information obtained from the outbound run. This in turn should favour reliance on the visual panorama and thus speed up calibration. Results did not support this prediction. At the displacement site, a delay had little effect on the extent of calibration or the speed of calibration (the number of trials to reach maximum calibration). Just before being displaced, ants were also tested in a test ring surrounded by high walls that obliterated the visual scenery. In the test ring, a delay made the ants less likely to rely on their vector: ants were often not oriented as a group. Otherwise, the ants in the test ring also did not calibrate any more or any faster. [ABSTRACT FROM AUTHOR]

Ambient air samples were collected in Brisbane (Australia), Dalian (China), and Hanoi (Vietnam) during Mar 2013-Feb 2018 using polyurethane foam based passive air samplers. A sampling rate calibration experiment was conducted for chlorinated paraffins (CPs, i.e., short-chain, medium-chain, and long-chain CPs), where the sampling rates were 4.5 ± 0.7, 4.8 ± 0.3, and 4.8 ± 2.1 m 3 day -1 for SCCPs, MCCPs, and LCCPs, respectively. The atmospheric concentration of CPs was then calculated and the medians of ∑CPs were 0.079, 1.0, and 0.89 ng m -3 in Brisbane, Dalian, and Hanoi, respectively. The concentration of CPs in Brisbane's air remained at low levels, with no significant differences observed between the city background site and the city center site, indicating limited usage and production of CPs in this city. The highest concentration of MCCPs was detected in Dalian, while the highest concentration of SCCPs was detected in Hanoi. A decrease of SCCP concentration and an increase of MCCPs' were found in Brisbane's air from 2016 to 2018, while increasing trends for both SCCPs and MCCPs were observed in Dalian. These results indicated impacts from different sources of CPs in the investigated cities.

Abstract In recent years, organic ultraviolet filters (UVFs) received considerable attention as a group of emerging contaminants, including in Australia where the use of UVFs is particularly relevant. Passive sampling using polymers has become widely used for routine monitoring of chemicals in the aquatic environment. Application of passive samplers for monitoring chemicals in the water relies on calibration data such as chemical's polymer-water partition coefficient (K pw) and diffusion coefficients in the sampling material (D p), for understanding uptake and kinetic limitations. In the present study, K pw and D p for nine UVFs were estimated. K pw values were determined in different water – polymer partition experiments where (1) a given mass of chemicals was dosed into the water and (2) into the polymer. Diffusion coefficients were determined using the stacking method. The estimated log K pw and log D p ranged from 2.9 to 6.4 L kg−1 and -11.1 to −10.5 m2s-1, respectively. The sufficient high D p allows application of kinetic models that only consider water boundary-controlled uptake for converting silicone sampler uptake into an aqueous phase concentration using the presented K pw. Graphical abstract Image 1 Highlights • Nine polymer-water partition coefficients of UV-filters are reported. • Nine diffusion coefficients of these UV-filters are reported. • Three UV filter partition & diffusion coefficients are reported for the first time. • The Single dose design is adequate to estimate the partition coefficients. [ABSTRACT FROM AUTHOR]

Highlights • Trajectory-based calibration of simulation model parameters for safety analysis is investigated. • Automated computer vision techniques are used to extract vehicle trajectories and traffic conflicts. • High correlation between simulated and field-measured conflicts was found after calibration. • The results show that using simulation to evaluate safety without proper calibration should be avoided. • The calibration parameters varied considerably among different traffic environments. Abstract The primary objective of this study is to investigate the relationship between field-measured conflicts and simulated conflicts estimated from the surrogate safety assessment model (SSAM) for two signalized intersections in Brisbane, Australia. Traffic video data collected from the two signalized intersections (left-hand traffic regulations) were used for analysis. Automated computer vision analysis techniques were used to extract vehicle trajectories and identify field-measured conflicts. Simulated conflicts were obtained using VISSIM and SSAM tool. A two-step calibration procedure was performed to test the correlation between simulated and field-measured conflicts. In addition, the spatial distributions of simulated and field-measured conflicts were compared. Finally, the calibration results for the left-hand traffic environment were compared with results for signalized intersections in Canada and China (right-hand traffic environment). The results indicated a good correlation between simulated and field-measured conflicts, especially at higher TTC thresholds. However, major differences between field-measured and simulated conflict spatial distributions indicate that despite the good correlation obtained via calibration, simulated conflicts do not adequately capture the actual conflict occurrence mechanism. It was also found that there were significant differences between the left-hand and right-hand traffic environments calibration results. [ABSTRACT FROM AUTHOR]

Objective: To develop a type 2 diabetes hip fracture risk tool in community-based patients, to validate it in an independent cohort, and to compare its performance against the only published prediction equation to include type 2 diabetes as a risk factor (QFracture).Research Design and Methods: Hip fracture hospitalizations in 1,251 participants with type 2 diabetes aged 40-89 years from the longitudinal Fremantle Diabetes Study Phase I (FDS1) were ascertained between entry (1993-1996) and end-2012. Competing risk regression modeling determined independent predictors of time to first fracture over 10 years and the coefficients incorporated in a risk model. The model was validated in 286 participants with type 2 diabetes from the Busselton Health Study (BHS).Results: Fifty FDS1 participants (4.0%) experienced a first hip fracture during 10,306 person-years of follow-up. Independent predictors of fracture were older age, female sex, lower BMI, peripheral sensory neuropathy, and estimated glomerular filtration rate <45 mL/min/1.73 m2. The model-predicted mean 10-year incident fracture risk was 3.3% with good discrimination, calibration, and accuracy. For a 3% cutoff, sensitivity was 76.0%, specificity 71.9%, positive predictive value (PPV) 10.1%, and negative predictive value (NPV) 98.6%. Model performance in the small BHS sample was also good (sensitivity 66.7%, specificity 79.8%, PPV 6.2%, and NPV 99.2%). QFracture performed well in FDS1 but required availability of 25 variables.Conclusions: The FDS1 hip fracture risk equation is a simple validated adjunct to type 2 diabetes management that uses variables that are readily available in routine care. [ABSTRACT FROM AUTHOR]

Early warning of increased algal activity is important to mitigate potential impacts on aquatic life and human health. While many methods have been developed to predict increased algal activity, an ongoing issue is that severe algal blooms often occur with low frequency in water bodies. This results in imbalanced data sets available for model specification, leading to poor predictions of the frequency of increased algal activity. One approach to address this is to resample data sets of increased algal activity to increase the prevalence of higher than normal algal activity in calibration data and ultimately improve model predictions. This study aims to investigate the use of resampling techniques to address the imbalanced dataset and determine if such methods can improve the prediction of increased algal activity. Three techniques were investigated, Kmeans under-sampling (US&#95;Kmeans), synthetic minority over-sampling technique (SMOTE), and 'SMOTE and cluster-based under-sampling technique' (SCUT). The resampling methods were applied to a Bayesian network (BN) model of Lake Burragorang in New South Wales, Australia. The model was developed to predict chlorophyll-a (chl-a) using a range of water quality parameters as predictors. The original data and each of the balanced datasets were used for BN structures and parameter learning. The results showed that the best graphical structure was obtained by adding synthetic data from SMOTE with the highest true positive rate (TPR) and area under the curve (AUC). When compared using a fixed graphical structure for the BN, all resampling techniques increased the ability of the BN to detect events with higher probability of increased algal activity. The resampling model results can also be used to better understand the most important influences on high chl-a concentrations and suggest future data collection and model development priorities., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

Supplemental digital content is available in the text. Purpose: To evaluate common modeling strategies in training load and injury risk research when modeling continuous variables and interpreting continuous risk estimates; and present improved modeling strategies. Method: Workload data were pooled from Australian football (n = 2550) and soccer (n = 23,742) populations to create a representative sample of acute:chronic workload ratio observations for team sports. Injuries were simulated in the data using three predefined risk profiles (U-shaped, flat and S-shaped). One-hundred data sets were simulated with sample sizes of 1000 and 5000 observations. Discrete modeling methods were compared with continuous methods (spline regression and fractional polynomials) for their ability to fit the defined risk profiles. Models were evaluated using measures of discrimination (area under receiver operator characteristic [ROC] curve) and calibration (Brier score, logarithmic scoring). Results: Discrete models were inferior to continuous methods for fitting the true injury risk profiles in the data. Discrete methods had higher false discovery rates (16%–21%) than continuous methods (3%–7%). Evaluating models using the area under the ROC curve incorrectly identified discrete models as superior in over 30% of simulations. Brier and logarithmic scoring was more suited to assessing model performance with less than 6% discrete model selection rate. Conclusions: Many studies on the relationship between training loads and injury that have used regression modeling have significant limitations due to improper discretization of continuous variables and risk estimates. Continuous methods are more suited to modeling the relationship between training load and injury. Comparing injury risk models using ROC curves can lead to inferior model selection. Measures of calibration are more informative judging the utility of injury risk models. [ABSTRACT FROM AUTHOR]

Accurate kicking is essential to team success in Australian football. It is not known how foot-ball impact characteristics influence kicking accuracy, nor is it known if variability in foot-ball impact characteristics is functional or dysfunctional to performance. The aim of this study was to identify the relationship between foot-ball impact characteristics and kicking accuracy and determine if variability in foot-ball impact characteristics influenced performance variability. Ten players performed 30 drop punt kicks toward a target with an Australian football ball. Kicking accuracy (measured as the horizontal distance from the target in the perpendicular direction of the kick), initial ball flight characteristics, and foot-ball impact characteristics, including a novel method to calculate impact location on the ball, were measured. Variability was indicated using standard deviation of foot-ball impact and ball flight characteristics. Multiple linear regression analysis identified azimuth ball flight trajectory as the most important ball flight characteristic influencing kicking accuracy, not ball flight characteristics associated with ball curve. Intra-individual multiple linear regressions identified azimuth ball impact location and foot-ball angle were the two most important factors explaining variance in azimuth ball flight trajectory, the chosen performance measure. Variability existed between and within players. Reduced variability in azimuth ball flight trajectory, the chosen performance measure, was associated with reduced variability in foot-ball impact characteristics. This result indicated variability in foot-ball impact characteristics was dysfunctional for performance in the analysed task. Foot-ball impact characteristics and variability in foot-ball impact characteristics influences accuracy of Australian football drop punt kicking. [ABSTRACT FROM AUTHOR]

The article focuses on quality assurance (QA) in exercise-physiology laboratories in Australia. QA is the planned systematic actions needed to provide adequate confidence by a service or product. The exercise-physiology-laboratory accreditation program, which is focused on the recognition of competent QA procedures, aims to promote continuous improvement in testing standards. Calibration and accuracy and test reliability, the key principles that underpin the accreditation program are discussed.

Background and Aims International quarantine rules prohibit exports of fruit from areas with fruit flies. Cold storage was tested from 1989 to 2012 as a postharvest treatment against the Mediterranean fruit fly (Medfly) in nine cultivars of Australian grown tablegrapes. The paper shows how market access was obtained. It reviews the methods, models and science that underpin fruit fly disinfestation. Methods and Results Tablegrapes were infested with immature stages of Medfly for large scale tests seeking 99.99% mortality at the upper 95% confidence level. Infested berries were exposed to 1, 2 and 3°C for 12, 13, 14, 15, 16, 18 and 20 days. No survivors were found in fruit held at 1°C for 16 days, 2°C for 18 days and 3°C for 20 days. Conclusion The data presented satisfy international requirements for access to quarantine-restricted markets. Several decades of research are reviewed to show how science and phytosanitary regulation can be used to harmonise cold disinfestation treatments. Significance of Study This work has enabled Australia's tablegrape industry to gain access to several international markets. The science used and presented is expected to benefit disinfestation research. [ABSTRACT FROM AUTHOR]

Undulatory underwater swimming (UUS) is one of the major skills contributing to performance in competitive swimming. UUS has two phases- the upbeat is performed by hip extension and knee flexion, and the downbeat is the converse action. The purpose of this study was to determine which kinematic variables of the upbeat and downbeat are associated with prone UUS performance in an elite sample. Ten elite participants were filmed performing three prone 20 m UUS trials. Seven landmarks were manually digitised to calculate eighteen kinematic variables, plus the performance variable- horizontal centre of mass velocity (VCOM). Mean VCOM was significantly correlated with body wave velocity (upbeat r = 0.81, downbeat r = 0.72), vertical toe velocity (upbeat r = 0.71, downbeat r = 0.86), phase duration (upbeat r = -0.79), peak hip angular velocity (upbeat r = 0.73) and mean knee angular velocity (upbeat r = -0.63), all significant at P < 0.05. A multiple stepwise regression model explained 78% of variance in mean VCOM. Peak toe velocity explained 72% of the variance, and mean body wave velocity explained an additional 6%. Elite swimmers should strive for a high peak toe velocity and a fast caudal transfer of momentum to optimise underwater undulatory swimming performance. [ABSTRACT FROM AUTHOR]