HTAPv3 mosaic: an emission inventory in support to Hemisperic Transport of Air Pollution
Bibliographic record
Abstract
The HTAP_v3 mosaic has been developed in the context of the UNECE Air Convention (UNECE Convention on Long-range Transboundary Air Pollution, link: https://unece.org/page404/env-lrtap) as a community effort to improve the scientific knowledge of the intercontinental transport of air pollution over the Northern Hemisphere (http://htap.org/). It consists of a global mosaic of monthly air pollutant (SO2, NOx, CO, NMVOCs, NH3, PM10, PM2.5, BC, OC) emission gridmaps at 0.1x0.1 degree resolution covering the time series 2000-2018 and all anthropogenic emission sectors, with the exception of Land Use, Land Use Change and Forestry. Emission gridmaps have been collected from officially reported data, and specifically from EMEP for Europe (CAMS-REG-v5.1), from the US Environmental Protection Agency (US EPA), Environment and Climate Change Canada (ECCC), REAS for most of the Asian domain, from Japan and CAPPS-KU for Korea. All remaining countries in the world have been covered using EDGARv6.1 air pollutant emission gridmaps. Furthermore, the EDGAR data have been used to complement the officially reported data as gapfilling source in case of missing data for certain sectors, pollutants and years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.019 |
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".