Are Fish Consumption Advisories for the Great Lakes Adequately Protective against Chemical Mixtures?
Bibliographic record
Abstract
BACKGROUND: The North American Great Lakes are home to > 140 types of fish and are famous for recreational and commercial fishing. However, the presence of toxic substances has resulted in the issuance of fish consumption advisories that are typically based on the most restrictive contaminant. OBJECTIVES: We investigated whether these advisories, which typically neglect the existence of a mixture of chemicals and their possible additive adverse effects, are adequately protective of the health of humans consuming fish from the Canadian waters of the Great Lakes. METHODS: Using recent fish contaminant monitoring data collected by the government of Ontario, Canada, we simulated advisories using most-restrictive-contaminant (one-chem) and multi-contaminant additive effect (multi-chem) approaches. The advisories from the two simulations were compared to determine if there is any deficiency in the currently issued advisories. RESULTS: Approximately half of the advisories currently issued are potentially not adequately protective. Of the four Great Lakes studied, the highest percentage of advisories affected are in Lake Ontario if an additive effect is considered. Many fish that are popular for consumption, such as walleye, salmon, bass and trout, would have noticeably more stringent advisories. CONCLUSIONS: Improvements in the advisories may be needed to ensure that the health of humans consuming fish from the Great Lakes is protected. In this region, total polychlorinated biphenyls (PCBs) and mercury are the major contaminants causing restrictions on consuming fish, whereas dioxins/furans, toxaphene, and mirex/photomirex are of minor concern. Regular monitoring of most organochlorine pesticides and metals in fish can be discontinued.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".