Lead exposure through consumption of big game meat in Quebec, Canada: risk assessment and perception
Bibliographic record
Abstract
Game meat from animals killed by lead ammunition may expose consumers to lead. We assessed the risk related to lead intake from meat consumption of white-tailed deer and moose killed by lead ammunition and documented the perception of hunters and butchers regarding this potential contamination. Information on cervid meat consumption and risk perception were collected using a mailed self-administrated questionnaire which was addressed to a random sample of Quebec hunters. In parallel, 72 samples of white-tailed deer (n = 35) and moose (n = 37) meats were collected from voluntary hunters and analysed for lead content using inductively coupled plasma-mass spectrometry. A risk assessment for people consuming lead shot game meat was performed using Monte Carlo simulations. Mean lead levels in white-tailed deer and moose killed by lead ammunition were 0.28 and 0.17 mg kg(-1) respectively. Risk assessment based on declared cervid meat consumption revealed that 1.7% of the surveyed hunters would exceed the dose associated with a 1 mmHg increase in systolic blood pressure (SBP). For consumers of moose meat once, twice or three times a week, simulations predicted that 0.5%, 0.9% and 1.5% of adults would be exposed to a dose associated with a 1 mmHg increase in SBP, whereas 0.9%, 1.9% and 3.3% of children would be exposed to a dose associated with 1 point intelligence quotient (IQ) decrease, respectively. For consumers of deer meat once, twice or three times a week, the proportions were 1.6%, 2.9% and 4% for adults and 2.9%, 5.8% and 7.7% for children, respectively. The consumption of meat from cervids killed with lead ammunition may increase lead exposure and its associated health risks. It would be important to inform the population, particularly hunters, about this potential risk and promote the use of lead-free ammunition.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".