Recent trends in the industrial use and emission of known and suspected carcinogens in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: In 2010, Ontario, Canada's most populous province, implemented its Toxics Reduction Act, Ontario Regulation 455/09 (TRA), which requires four major manufacturing and mineral processing industry groups that already report releases of pollutants federally to the National Pollutant Release Inventory to additionally track, account and report their use and creation. The TRA was modeled after the Massachusetts Toxics Use Reduction Act of 1989, which has been very successful and reported significant reduction in toxic use and carcinogen release. METHODS: Data from the TRA were retrieved, and the trends in the use and release of 17 known and suspected carcinogens associated with the seven most prevalent cancers diagnosed in Ontario and reported by industrial facilities in Ontario from 2011 to 2015 were examined using methodology adapted from (Jacobs MM, Massey RI, Tenney H, Harriman E. Reducing the use of carcinogens: the Massachusetts experience. Rev Environ Health 2014;29(4):319-40). RESULTS: Carcinogens associated with lung cancers, leukemia and lymphomas were observed as the most used and released carcinogens in Ontario by amount. Overall, for 2011-2015, there was an observed reduction in the industrial use of carcinogens, except among breast carcinogens, which increased by 20%. An increase in the industrial releases of carcinogens was observed across all cancer sites, except among lung carcinogens, which decreased by 28%. CONCLUSION: The results of this study highlight the potential for reducing the cancer burden by reducing the use and release of select carcinogens associated with particularly prevalent cancers. Toxics use reduction programs can support cancer prevention initiatives by promoting targeted reductions in exposures to industrial carcinogens.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".