Search

Recent decades have seen an exponential rise in the application of machine learning in geoscience. Fundamental differences distinguish geoscience data from most other data types. Geoscience datasets are typically multi-dimensional, and contain 1-D (drillholes), 2-D (maps or cross-sections), and 3-D volumetric and point data (models/voxels). Geoscience data quality is a product of its resolution and the precision of the methods used to acquire it. The dimensionality, resolution, and precision of each layer within a geoscience dataset translates to limitations in spatiality, scale and uncertainty of resulting interpretations. Historically, geoscience datasets were overlaid cartographically, to incorporate subjective, experience-driven knowledge, and variances in scale, and resolution. The nuances and limitations that underpin the reliability of automated interpretation are well understood by geoscientists, but are rarely appropriately transferred to data science. However, for true integration of geoscience data, such issues cannot be overlooked without consequence. To apply data analytics to complex geoscience data (e.g., hydrothermal mineral systems) effectively, methodologies must be used that characterise the system quantitatively, using collocated analyses, at a common scale. This paper provides research and exploration insights from an innovative district-wide, scale-integrated, geoscience data project, which analysed 1,590 samples from 23 mineral deposits and prospects across the Cloncurry District, Queensland, Australia. Ten different analytical techniques, including density, magnetic susceptibility, remanent magnetisation, anisotropy of magnetic susceptibility, radiometrics, conductivity, scanning electron microscopy (SEM)-based automated mineralogy, geochemistry, and short-wave infrared (SWIR) hyperspectral data with 561 columns of scale-integrated data (+2151 columns of SWIR). All data were collected on 2 cm x 2.5 cm sample cylinders; a scale at which the confidence in coupling of data from techniques can be high. These data are integrated by design, to eliminate the need to downscale coarser measurements via assumptions, inferences, inversions, and interpolations. This scale-consistent approach is critical to the quantitative characterisation of mineral systems and has numerous applications to mineral exploration, such as linking alteration paragenesis with structural controls and petrophysical zonation. [ABSTRACT FROM AUTHOR]

Background: Timely publication of clinical trials is critical to ensure the dissemination and implementation of high-quality healthcare evidence. This study investigates the publication rate and time to publication of randomized controlled trials (RCTs) registered in the Australian New Zealand Clinical Trials Registry (ANZCTR)., Materials and Methods: We conducted a cross-sectional study of RCTs registered with the ANZCTR in 2007, 2009, and 2011. Multiple bibliographic databases were searched until October 2021 to identify trial publications. We then calculated publication rates, proportions, and the time to publish calculated from the date of first participation enrolment to publication date., Results: Of 1,970 trial registrations, 541 (27%) remained unpublished 10 to 14 years later, and the proportion of trials published decreased by 7% from 2007 to 2011. The average time to publish was 4.63 years. The prospective trial registration rate for 2007, 2009 and 2011 was 48% (952 trials) and over this time there was an increase of 19% (280 prospective trials). Trials funded by non-Industry organizations were more likely to be published (74%, 1204/1625 trials) than the industry-funded trials (61%, 224/345 trials). Larger trials with at least 1000 participants were published at a rate of 88% (85/97 trials) and on average took 5.4 years to be published. Smaller trials with less than 100 participants were published at a lower rate with 67% (687/1024 trials) published and these trials took 4.31 years on average to publish., Conclusions: Just over a quarter of all trials on the ANZCTR for 2007, 2009, and 2011 remain unpublished over a decade later. The average time to publication of nearly five years may reflect the larger trials which will have taken longer to recruit participants. Over half of study sample trials were retrospectively registered, but prospective registration improved over time, highlighting the role of mandating trial registration., Competing Interests: The authors have declared that no competing interests exist, (Copyright: © 2023 Showell et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)