Marine Mammals as Indicators of Environmental Pollution and Potential Health Effects
Bibliographic record
Abstract
The study and protection of environmental and human health is complex given the variety of anthropogenic and natural stressors threatening the well-being of exposed organisms. Researchers have turned to wild animals as sentinel species to study the critical questions relating to environmental chemical contamination and potential adverse health effects of contaminant exposure. Marine mammals are one group of animals that are particularly suited as indicators of environmental health because of their long lifespan, high trophic level, spatial distribution at various scales, and propensity to accumulate and respond to environmental contaminants. This chapter discusses how marine mammals are used to monitor and identify chemical pollutants of concern and determine potential health effects on practically all vertebrate physiological systems and across biological scales, from the molecular to the population level. We highlight the diversity of study designs, pollutant classes, methodological tools, and unique insights gained on source, transport, fate, and health effects of contaminants from studies of marine mammal toxicology to showcase the usefulness of these sentinel species as indicators in ecotoxicology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".