Toxic chemicals and their impacts in the St. Lawrence Estuary and Saguenay Fjord,Quebec, Canada: from a chemical to an ecosystem-based risk manage
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.The St. Lawrence Estuary (SLE) and Saguenay Fjord (SF), Quebec, Canada, have received world-wide attention in the early 1980s when high concentrations of contaminants and high prevalence of lesions including neoplasia, hermaphrodism and infection by opportunistic agents were reported in beluga whales (Delphinapterus leucas). Both persistent organic pollutants (POPs) such as PCBs mainly originating from the upstream industrialized sectors of the Great Lakes and the upper St. Lawrence River, and local contamination by polycyclic aromatic hydrocarbons (PAHs) and mercury have been incriminated. The release of these chemicals has been successfully reduced through environmental regulations and restoration of contaminated areas. Since 1970s, declines in mercury, PAHs and PCBs have been observed in sediments and in biota. However, organisms remain exposed to complex mixtures of contaminants including regulated persistent compounds remaining in the ecosystem and newer compounds which also have the potential of causing deleterious effects. Interactions between toxic chemicals and other environmental stressors may increase the risk of deleterious impacts. New concerns include: chronic inputs of tributyltin from ship transportation associated with reproductive/immune disturbances, interaction between nutritional condition and POPs, increasing concentrations of brominated flame retardants in beluga tissues and inputs of agricultural chemicals and nutrients from SLE tributaries. Several fish populations historically used as preys by the beluga are declining as a consequence of multiple anthropogenic factors. Moreover hypoxic area in the bottom of the SLE is increasing and could act as an additional stressor. An ecosystem-based approach is being developed to pursue the protection the SLE/SF ecosystems facing multiple stressors and variable environmental conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".