Geographic exposure and risk assessment for food contaminants in Canada
Bibliographic record
Abstract
The purpose of this thesis is to explore differences in lifetime excess cancer risk (LECR) for Canadians from intake of contaminants in food and beverages based on geographic location, gender and income levels. A probabilistic risk assessment approach (Monte Carlo simulation) was used to estimate the range and frequency of possible daily contaminant intakes for Canadians, and associate these intake levels with lifetime excess cancer risk. Monte Carlo risk simulation was applied to estimate probable contaminant intake and associated lifetime excess cancer risk from arsenic, benzene, lead, polychlorinated biphenyls (PCBs) and tetrachloroethylene (PERC) in 60 whole foods from the dietary patterns of 34,944 Canadians from 10 provinces, as derived from Health Canada’s Canadian Community Health Survey, Cycle 2.2, Nutrition (2004)1. These results were compared to the current Health Canada guideline that suggests that 10 extra cancers per one million people is a negligible risk. Of the 5 contaminants tested in my model arsenic showed the greatest difference between urban and rural estimated lifetime excess cancer risk, although extra cancers in both rural and urban Canada were predicted from exposure to PCB and benzene. Lifetime excess cancer risk is estimated to be higher for men in Canada for all five contaminants, with an emphasis on males in British Columbia compared to females from the dietary intake of arsenic. When based on income level, my model predicts extra cancers higher for low and middle incomes from dietary exposures to arsenic, benzene, lead and PERC, however, high income populations are more likely to have extra cancers from dietary intake of PCBs.
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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) | 0.000 | 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".