Chemical–environment interactions affecting the risk of impacts on aquatic organisms: A review with a Canadian perspective — interactions affecting vulnerability
Bibliographic record
Abstract
Environmental change can increase the vulnerability of aquatic species to toxic chemicals by challenging an organism’s aptitude to respond to chemicals or to repair toxic injury or by modifying animal behaviours like migration or predation. On the other hand, xenobiotics may affect the capacity of aquatic species to adapt to environmental challenges that come with change (e.g., pathogens, temperature). Across Canada we have identified a number of circumstances where chemicals and environmental variability have likely worked together to affect vulnerability of aquatic organisms. For example in the Maritimes, exposure to municipal wastewater or bleached kraft pulp mill effluent altered immune function in bivalves and increased their risk of developing haemocytic neoplasia, a disease known to cause high mortality. Northwest Atlantic cod stocks have experienced large-scale changes in environment and exhibit marked seasonal cycles in energy reserves. The risk associated with subsequent redistribution of persistent chemicals in the body together with nutritional deficiency is presently under evaluation since it could affect the recovery of these endangered stocks. In the Great Lakes, the introduction of an invasive fish species, the alewife, modified the diet of salmonids, which led to a deficiency of the vitamin thiamine in eggs causing early mortality. Contaminants may interact with thiamine deficiency and thus critically impair recruitment of salmonids. Viewing the risks presented by toxic chemicals from the point of view of species vulnerability, offers managers opportunities to mitigate such risks, for example, through habitat, ocean and fisheries management. Further research is needed to develop biomarkers of vulnerability, identify most vulnerable life stages and populations, to understand the interactions between global environmental changes, nutritional status, pathogens and toxic chemicals, and to develop integrated approaches to manage vulnerability of aquatic ecosystems to 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".