Exposing Canada's chemical valley : an investigation of cumulative air pollution emissions in the Sarnia, Ontario area
Bibliographic record
Abstract
Nearly 40 per cent of Canada's chemical industry is clustered near the town of Sarnia, Ontario, which is now considered one of the most polluted places in Canada. In 2005, facilities located near the Sarnia area emitted more than 131 million kg of national pollutant release inventory chemicals. Facilities in the area emitted 16.5 million tonnes of greenhouse gases (GHGs) as well as 5.7 million kg of toxic air pollutants known to cause cancer and endocrine disorders in humans. The cumulative emissions produced from facilities in the region as well as from facilities in the nearby United States have made the region Ontario's worst air pollution hotspot. The emissions are now impacting the health of both the residents of Sarnia and the Aamjiwnaang First Nation. Local ecosystems have also been compromised. This report highlighted strategies that may be used to reduce emissions in the region. The report reviewed various aggressive pollution prevention strategies, as well as the enforcement of existing laws and regulatory standards. It was concluded that federal and local governments, as well as First Nations must take the necessary steps to improve and protect the health of communities in the region. 11 refs., 15 tabs., 14 figs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".