Bibliographic record
Abstract
LARGER INDUSTRIAL facilities in the U.S. and Canada are doing a better job of reducing releases of toxic chemicals than are small and medium-sized plants, says an analysis released on Oct. 18. Cuts in waste generation by large facilities drove the drop in toxic releases in the U.S. and Canada between 1998 and 2004, according to an annual report from the North American Commission on Environmental Cooperation (CEC). The analysis combines data from the U.S. Toxics Release Inventory (TRI) and Canada’s National Pollutant Release Inventory. Big facilities have cut waste and emissions, but the same is not true of smaller plants. While they individually release smaller amounts of toxics than a big plant, small and mid-sized facilities as a group substantially increased their releases between 1998 and 2004, the analysis says. Large emitters, the report says, tend to participate in pollution prevention programs, while smaller facilities are less likely to do so. “Industry and government ...
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.597 | 0.353 |
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".