Bibliographic record
Abstract
Recent articles highlighting potential weakening of air pollution regulations in the United States should be a cause for concern for public health worldwide. Environmental regulations to curb air pollution, particularly fine-particle pollution, should be based on sound scientific evidence, not politics. Unfortunately, members of the public seldom read scientific articles published in reputable journals, but they do listen to politicians. However, members of the public can learn more about atmospheric pollutant releases, including fine-particulate matter from industrial facilities under ‘right-to-know’ legislation and public disclosure principles, using Pollutant Release and Transfer Registers (PRTRs). PRTRs are a key policy tools designed to curb air pollution and are used widely in many countries and help support enforcement of environmental pollution control regulations. The US Environmental Protection Agency (US EPA) launched the first PRTR, the Toxic Release Inventory (TRI) in 1987 and Canada followed suit with the National Pollutant Release Inventory (NPRI) in 1993. Whilst PRTRs have been criticised for data accuracy and under reporting, they are still effective tools to curb air pollution through increased public understanding and engagement in decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.080 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.029 |
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".