Chemical–environment interactions affecting the risk of impacts on aquatic organisms: A review with a Canadian perspective — interactions affecting exposure
Bibliographic record
Abstract
As a consequence of human activity, the variability and range of environmental conditions is increasing. We review how the interactions between toxic chemicals and environmental change may affect exposure of aquatic organisms to stressful conditions and therefore alter the risk of deleterious impacts. Even in the absence of new inputs of contaminants, changing environmental conditions alters the transport, transformation and distribution of contaminants and their bioavailability. Conversely, some toxic chemicals modify the exposure of aquatic species to other stressors by affecting species distribution, behaviour or habitat. Across Canada there are a number of specific examples where interactions between contaminants and environmental change are probably harming aquatic species. In the Arctic, change in foraging brought on by change in ice regime, is a plausible mechanism to explain the marked recent increase in mercury concentrations in Beaufort Sea beluga whales. On the Pacific coast, chemical exposure by itself or in combination with other environmental factors, is a leading suspect for altered migration timing of some salmon stocks in the Fraser River leading to massive pre-spawning mortality. In the North Atlantic, short-term exposure of Atlantic salmon to endocrine-disrupting substances in their freshwater natal environments later leads to detectable effects at the time of their migration to saltwater. In Alberta, biotic and abiotic characteristics of the habitat dramatically affect exposure pathways and the risk of toxic effects of selenium in early life stages of trout. A better understanding of the interactions between toxic chemicals and environmental factors is a fundamental requirement for efficient management and protection of aquatic ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; both teacher heads agree on what is shown here.
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".