Chemical risks associated with consumption of shellfish harvested on the north shore of the St. Lawrence River's lower estuary.
Bibliographic record
Abstract
Shellfish have the capacity to accumulate chemical contaminants found in their biotope and therefore present a potential risk for consumers. This study was conducted to assess the chemical risks associated with consumption of shellfish harvested on the north shore of the St. Lawrence River's lower estuary. A survey was carried out on 162 recreational harvesters, and shellfish were sampled for chemical contaminant analysis. We quantified 10 metals, 22 polycyclic aromatic hydrocarbons (PAHs), 14 polychlorinated biphenyls (PCBs), and 10 chlorinated pesticides. We subsequently evaluated cancer and noncancer risks for four consumption scenarios based on our survey results and published results. Soft-shell clams (Mya arenaria) were by far the most consumed shellfish species. Of the 56 selected contaminants, 36 were detected in the 23 homogenates of soft-shell clam meat. None of the contaminants found in the soft-shell clams were associated with intakes that exceed the main exposure limit recommendations proposed to prevent noncancer effects. However, several limits must be considered before drawing conclusions about the relative safety of shellfish consumption regarding this end point. Furthermore, inorganic arsenic and PCBs were present in sufficient concentrations to lead to cancer risks exceeding the level often considered acceptable for environmental exposure (1 x 10 (-4) to 1 x 10(-6)) in each of the four scenarios, even for the lowest observed scenario of 15 meals of soft-shell clams per year.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".