Atmospheric Composition Analysis Group (ACAG) Global/Regional Estimates
Bibliographic record
Abstract
The Atmospheric Composition Analysis Group (ACAG) Global/Regional Estimates (V5.GL.04) dataset presents estimated monthly and annual average concentrations of fine particulate matter (PM2.5) air pollution. The dataset provides global coverage during 1998 to 2022 with 0.01 degree (~1 km) and 0.1 degree (~10 km) spatial resolution. Summary data tables are also available at the national scale for all countries and at the sub-national scale (e.g., for states, provinces, or regions) for Canada, China, India, and the United States. Concentrations are estimated using satellite-based air pollution observations combined with a chemical transport model and subsequent calibration to ground-based air pollution observations using geographically weighted regression. This dataset can be used to study monthly or seasonal variations in PM2.5 concentrations and is intended for use in large-scale epidemiological studies and health impact assessments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.043 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".