The trouble with salmon: relating pollutant exposure to toxic effect in species with transformational life histories and lengthy migrations
Bibliographic record
Abstract
The control of point-source contaminants and regulations designed for specific waste discharges have reduced incidents of fish kills. These actions, however, do not protect fish like salmon, which encounter many different contaminants during extensive migrations. Attempts to document pollutant-associated toxicity is challenging in migratory salmon, although a few laboratory and field studies have produced a convincing body of evidence that lifelong contaminant exposure can contribute to the demise of fish. The case of the decline of Fraser River sockeye salmon (Oncorhynchus nerka) in British Columbia, Canada, brought into sharp relief the difficulty of assigning a specific cause (e.g., climate, disease, or contaminants) to a diffuse problem (i.e., low fish returns). Determining the effects that pollutants have on wild salmon requires study designs that consider life history, habitat, and the real world of complex contaminant exposures. In the absence of evidence from such study designs, the future survival of salmon may hinge on the application by managers of the precautionary approach to stressors that are within immediate jurisdictional control, such as toxic chemicals.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".