Supplementary Data for "Declines in anthropogenic mercury emissions in the Global North and China offset by the Global South"
Bibliographic record
Abstract
Supplementary Data 1: Country technical groupings for determining emission factors of Hg. WEurope - Western Europe. ECEurope - Eastern and Central Europe. U.S.&CA - United States and Canada. RON - the rest of the Global North. SSA - Sub-Saharan Africa. NAME - Northern Africa and the Middle East. LATIN - Latin America. ROS - the rest of the Global South.Supplementary Data 2: The parameters of the abatement factor of sigmoid curves. PP - power plant combustion. IND - industry combustion. DR - commercial/residential/ transportation/agriculture combustion.Supplementary Data 3: Base year ASM population and data qualities by countries. ASM - artisanal and small-scale mining. ASGM - artisanal and small-scale gold mining.Supplementary Data 4: Gridded global Hg emission data for 1960 and 2021 (0.1°×0.1°). The NetCDF file Supplementary_Data_4.nc contains gridded data representing global Hg emissions for the years 1960 and 2021. It includes dimensions for latitude (1800 points, 90°N to 90°S), longitude (3600 points, 180°W to 180°E) and year (2 points, 1960 and 2021). The variables are latitude (values in degrees north), longitude (values in degrees east), year (1960 and 2021), and emissions (Hg emissions data in units of Mg per grid cell).
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.763 | 0.306 |
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".