Cancer Risk Assessment of PM$_{2.5}$-bound Elements in Windsor, Canada during 2004−2018
Bibliographic record
Abstract
This study estimated incremental cancer risks due to inhalation exposure to six PM2.5-bound elements, As, Cd, Co, Cr, Ni, and Pb, in Windsor, Ontario, Canada using concentrations collected from 2004 to 2018.The overall cancer risk was 6.3×10 -6 .During the 15-year study period, the risks decreased from 8.5×10 -6 in 2004 to 4.8×10 -6 in 2018, because of declining concentrations of the six elements, except for As.As was the largest contributor (69%) to cancer risk throughout the study period, followed by Cr(VI) (15%) , Pb (5.3%), Cd (4.7%), Co (3.2%), and Ni (3.1%).This is because both the concentrations and toxicity of As are the second highest among the six elements.A seasonal trend was observed, with higher cancer risks in summer (7.7×10 -6 ) and fall (7.3×10-6 ) and lower in spring (5.7×10 -6 ) and winter (4.7×10 -6 ).None of those risks exceeded the USEPA's tolerate level of 1×10 -4 , indicating an acceptable risk.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".