Background: International medical graduates (IMGs) are an essential workforce for many high-income countries worldwide and are often recruited to fill workforce shortages. Studies identify workplace discrimination as a major challenge for IMGs. However, little detailed exploration has been undertaken on this issue.
View Article and Find Full Text PDFObservations from the NASA Global Ecosystem Dynamics Investigation (GEDI) provide global information on forest structure and biomass. Footprint-level predictions of aboveground biomass density (AGBD) in the GEDI mission are based on training data sourced from sparsely distributed field plots coincident with airborne laser scanning surveys. National Forest Inventories (NFI) are rarely used to calibrate GEDI footprint biomass models because their sampling and positional accuracy prevent accurate colocation with GEDI or ALS.
View Article and Find Full Text PDFRecent global policy initiatives aimed at reducing forest degradation require practical definitions of degradation that are readily monitored. However, consistent approaches for monitoring forest degradation over the long term and at broad scales are lacking. We quantified the long-term effects of intensive wood harvest on above-ground carbon and biodiversity at fine resolutions (30 m) and broad scales (New Brunswick, Canada; 72,908 km).
View Article and Find Full Text PDFAboveground biomass density (AGBD) estimates from Earth Observation (EO) can be presented with the consistency standards mandated by United Nations Framework Convention on Climate Change (UNFCCC). This article delivers AGBD estimates, in the format of Intergovernmental Panel on Climate Change (IPCC) Tier 1 values for natural forests, sourced from National Aeronautics and Space Administration's (NASA's) Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud and land Elevation Satellite (ICESat-2), and European Space Agency's (ESA's) Climate Change Initiative (CCI). It also provides the underlying classification used by the IPCC as geospatial layers, delineating global forests by ecozones, continents and status (primary, young (≤20 years) and old secondary (>20 years)).
View Article and Find Full Text PDFObjectives: The generation of structured documents for clinical trials is a promising application of large language models (LLMs). We share opportunities, insights, and challenges from a competitive challenge that used LLMs for automating clinical trial documentation.
Materials And Methods: As part of a challenge initiated by Pfizer (organizer), several teams (participant) created a pilot for generating summaries of safety tables for clinical study reports (CSRs).