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NASA's Orbiting Carbon Observatories (OCO-2 and OCO-3) provide measurements of column-averaged carbon dioxide concentrations (XCO2) with sufficient spatial resolution and precision to constrain bottom-up estimates of CO2 fluxes at regional scales. We use Bayesian inversion methods assimilating satellite retrievals to improve estimates of CO2 fluxes in the South Coast Air Basin (SoCAB) which surrounds Los Angeles, and in the San Francisco Bay Area Air Basin (SFBA). We study 2020 to understand the impact of the COVID-19 lockdowns and an active wildfire season. Our results indicated that a 50% (30%) reduction in CO2 emissions relative to 2015 during the COVID-19 lockdown period was consistent with OCO measurements for SFBA (SoCAB). We find that posterior wildfire emissions differed significantly from the prior at the scale of individual wildfires, though with large uncertainties, and that wildfire emissions in SFBA are significant, attributing 72% of the region's CO2 emissions during August 2020 to wildfires.

It is desirable for an educational assessment to be constructed of items that can differentiate different performance levels of test takers, and thus it is important to estimate accurately the item discrimination parameters in either classical test theory or item response theory. It is particularly challenging to do so when the sample sizes are small. The current study reexamined the relationship between the biserial correlation coefficient and the discrimination parameter to investigate whether the biserial correlation coefficient estimator could be modified and whether biserial-based estimators could be used as alternate estimates of the item discrimination indices. Results show that the modified and alternative approaches work slightly better under certain circumstances (e.g., for small sample sizes or shorter tests), assuming normality of the latent ability distribution. Applications of these alternative estimators are presented in item scaling and weighted differential item functioning analyses. Recommendations and limitations are discussed for practical use of these proposed methods.

Score differencing is one of the six categories of statistical methods used to detect test fraud (Wollack & Schoenig, 2018) and involves the testing of the null hypothesis that the performance of an examinee is similar over two item sets versus the alternative hypothesis that the performance is better on one of the item sets. We suggest, to perform score differencing, the use of the posterior probability of better performance on one item set compared to another. In a simulation study, the suggested approach performs satisfactory compared to several existing approaches for score differencing. A real data example demonstrates how the suggested approach may be effective in detecting fraudulent examinees. The results in this article call for more attention to the use of posterior probabilities, and Bayesian approaches in general, in investigations of test fraud. [For the corresponding grantee submission, see ED613529.]

With the increasing use of automated scores in operational testing settings comes the need to understand the ways in which they can yield biased and unfair results. In this paper, we provide a brief survey of some of the ways in which the predictive methods used in automated scoring can lead to biased, and thus unfair automated scores. After providing definitions of fairness from machine learning and a psychometric framework to study them, we demonstrate how modeling decisions, like omitting variables, using proxy measures or confounded variables, and even the optimization criterion in estimation can lead to biased and unfair automated scores. We then introduce two simple methods for evaluating bias, evaluate their statistical properties through simulation, and apply to an item from a large-scale reading assessment.

The focus of this paper is on the empirical relationship between item difficulty and item discrimination. Two studies--an empirical investigation and a simulation study--were conducted to examine the association between item difficulty and item discrimination under classical test theory and item response theory (IRT), and the effects of the association on various quantities of interest. Results from the empirical investigation show that item difficulty and item discrimination are negatively correlated under classical test theory, mostly negatively correlated under the two-parameter logistic model, and mostly positively correlated under the three-parameter logistic model; the magnitude of the correlation varied over the different data sets. Results from the simulation study reveal that a failure to incorporate the correlation between item difficulty and item discrimination in IRT simulations may provide the investigator with inaccurate values of important quantities of interest, and may lead to incorrect operational decisions. Implications to practice and future directions are discussed.

We study how a regulator can best allocate its limited inspection resources. We direct our analysis to a US Occupational Safety and Health Administration (OSHA) inspection program that targeted dangerous establishments and allocated some inspections via random assignment. We find that inspections reduced serious injuries by an average of 9% over the following five years. We use new machine learning methods to estimate the effects of counterfactual targeting rules OSHA could have deployed. OSHA could have averted over twice as many injuries if its inspections had targeted the establishments where we predict inspections would avert the most injuries. The agency could have averted nearly as many additional injuries by targeting the establishments predicted to have the most injuries. Both of these targeting regimes would have generated over $1 billion in social value over the decade we examine. Our results demonstrate the promise, and limitations, of using machine learning to improve resource allocation. JEL Classifications: I18; L51; J38; J8

Atmospheric deposition is a source of potentially bioavailable iron (Fe) and thus can partially control biological productivity in large parts of the ocean. However, the explanation of observed high aerosol Fe solubility compared to that in soil particles is still controversial, as several hypotheses have been proposed to explain this observation. Here, a statistical analysis of aerosol Fe solubility estimated from four models and observations compiled from multiple field campaigns suggests that pyrogenic aerosols are the main sources of aerosols with high Fe solubility at low concentration. Additionally, we find that field data over the Southern Ocean display a much wider range in aerosol Fe solubility compared to the models, which indicate an underestimation of labile Fe concentrations by a factor of 15. These findings suggest that pyrogenic Fe-containing aerosols are important sources of atmospheric bioavailable Fe to the open ocean and crucial for predicting anthropogenic perturbations to marine productivity.

We present ReactionMechanismSimulator.jl (RMS), a modern differentiable software for the simulation and analysis of chemical kinetic mechanisms, including multiphase systems. RMS has already been applied to problems in combustion, pyrolysis, polymers, pharmaceuticals, catalysis, and electrocatalysis. RMS is written in Julia, making it easy to develop and allowing it to take advantage of Julia's extensive numerical computing ecosystem. In addition to its extensive library of optimized analytic Jacobians, RMS can generate and use Jacobians computed using automatic differentiation and symbolically generated analytic Jacobians. RMS is demonstrated to be faster than Cantera and Chemkin in several benchmarks. RMS also implements an extensive set of features for analyzing chemical mechanisms, including a library of easy‐to‐call plotting functions, molecular structure resolved flux diagram generation, crash analysis, traditional sensitivity analysis, transitory sensitivity analysis, and an automatic mechanism analysis toolkit. RMS implements efficient adjoint and parallel forward sensitivity analyses. We also demonstrate the ease of adding new features to RMS. [ABSTRACT FROM AUTHOR]

Detailed chemical kinetic mechanisms are necessary for resolving many important chemical processes. As the chemistry of smaller molecules has become better grounded and quantum chemistry calculations have become cheaper, kineticists have become interested in constructing progressively larger kinetic mechanisms to model increasingly complex chemical processes. These large kinetic mechanisms prove incredibly difficult to refine and time‐consuming to interpret. Traditional sensitivity analysis on a large mechanism can range from inconvenient to practically impossible without special techniques to reduce the computational cost. We first present a new time‐local sensitivity analysis we term transitory sensitivity analysis. Transitory sensitivity analysis is demonstrated in an example to accurately identify traditionally sensitive reactions at an 18,000x speed up over traditional sensitivities. By fusing transitory sensitivity analysis with more traditional time‐local branching, pathway, and cluster analyses, we develop an algorithm for efficient automatic mechanism analysis. This automatic mechanism analysis at a time point is able to identify the reactions a target is most sensitive to using transitory sensitivity analysis and then propose hypotheses why the reaction might be sensitive using branching, pathway, and cluster analyses. We implement these algorithms within the reaction mechanism simulator (RMS) package, which enables us to report the automatic mechanism analysis results in highly readable text formats and in molecular flux diagrams. [ABSTRACT FROM AUTHOR]

This commentary examines the Duolingo English Test Responsible AI standards and provides some thoughts on specific ways we can evaluate the use of AI for automated scoring. [ABSTRACT FROM AUTHOR]

One common score reported from diagnostic classification assessments is the vector of posterior means of the skill mastery indicators. As with any assessment, it is important to derive and report estimates of the reliability of the reported scores. After reviewing a reliability measure suggested by Templin and Bradshaw, this article suggests three new measures of reliability of the posterior means of skill mastery indicators and methods for estimating the measures when the number of items on the assessment and the number of skills being assessed render exact calculation computationally burdensome. The utility of the new measures is demonstrated using simulated and real data examples. Two of the suggested measures are recommended for future use.

Abstract. This work reports on the current status of the global modeling of iron (Fe)deposition fluxes and atmospheric concentrations and the analyses of thedifferences between models, as well as between models and observations. Atotal of four global 3-D chemistry transport (CTMs) and general circulation(GCMs) models participated in this intercomparison, in the framework ofthe United Nations Joint Group of Experts on the Scientific Aspects of MarineEnvironmental Protection (GESAMP) Working Group 38, “The Atmospheric Inputof Chemicals to the Ocean”. The global total Fe (TFe) emission strength inthe models is equal to ∼72 Tg Fe yr−1 (38–134 Tg Fe yr−1)from mineral dust sources and around 2.1 Tg Fe yr−1 (1.8–2.7 Tg Fe yr−1)from combustion processes (the sum of anthropogeniccombustion/biomass burning and wildfires). The mean global labile Fe (LFe)source strength in the models, considering both the primary emissions and theatmospheric processing, is calculated to be 0.7 (±0.3) Tg Fe yr−1,accounting for both mineral dust and combustion aerosols. Themean global deposition fluxes into the global ocean are estimated to be in the rangeof 10–30 and 0.2–0.4 Tg Fe yr−1 for TFe and LFe, respectively,which roughly corresponds to a respective 15 and 0.3 Tg Fe yr−1 for the multi-model ensemble model mean. The model intercomparison analysis indicates that the representation of theatmospheric Fe cycle varies among models, in terms of both the magnitude ofnatural and combustion Fe emissions as well as the complexity of atmosphericprocessing parameterizations of Fe-containing aerosols. The model comparisonwith aerosol Fe observations over oceanic regions indicates that most modelsoverestimate surface level TFe mass concentrations near dust sourceregions and tend to underestimate the low concentrations observed in remoteocean regions. All models are able to simulate the tendency of higher Feconcentrations near and downwind from the dust source regions, with the meannormalized bias for the Northern Hemisphere (∼14), largerthan that of the Southern Hemisphere (∼2.4) for the ensemble modelmean. This model intercomparison and model–observation comparison studyreveals two critical issues in LFe simulations that require furtherexploration: (1) the Fe-containing aerosol size distribution and (2) therelative contribution of dust and combustion sources of Fe to labile Fe inatmospheric aerosols over the remote oceanic regions.