Additional burden of cancers due to environmental carcinogens in Newfoundland and Labrador: a spatial analysis
Bibliographic record
Abstract
Several environmental carcinogens are found to be spread across wide geographic areas, and the exposed inhabitants are at risk of developing various types of cancers. Arsenic and disinfection by-products in drinking water, ultraviolet rays from the sun, and agricultural chemicals used in golf courses were found to be the possible cancer risks. The study aimed to estimate the risks of cancer due to exposure to environmental carcinogens known to be present in wide geographic areas in Newfoundland and Labrador (NL). The NL cancer care registry provided 2008–2017 data (histological diagnosis, age, sex, and six-digit postal code) on cancers relevant to arsenic, disinfection by-products , ultraviolet rays , and agricultural chemical exposures. The geographic distribution of environmental carcinogens was collected from government sources and previous studies. Risk ratios (RR) of annual prevalence rates of cancers in high-risk (exposed to environmental carcinogens) and low-risk populations. For ultraviolet rays , arsenic, disinfection by-products , and agricultural chemicals, the RR (95% CI) were 1.5 (1.4–1.6), 1.25 (1.03–1.51), 1.8 (1.67–1.94), and 1.49 (1.3–1.7), respectively. An excess number of cancers in high-risk areas was possibly associated with exposure to environmental carcinogens . Public health regulations, environmental monitoring, health promotion, and increased awareness in high-risk areas can prevent exposure to environmental carcinogens.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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".