Responses of vulnerable fishes to environmental stressors in the Canadian Great Lakes basin<sup>1</sup>
Bibliographic record
Abstract
Quantifying the responses of rare and vulnerable species to environmental stressors poses special challenges. This study examined the responses of vulnerable fish species listed under the Species at Risk Act to environmental stressors in lakes, streams, and wetlands of the Canadian Great Lakes basin. We used a joint species distribution model (JSDM) to improve the estimates of responses of vulnerable species to environmental stressors, and the effects of functional traits on those responses, by “borrowing information” from abundant species for which we have more information. We measured abundance, functional traits, and taxonomic relationships for 115 freshwater fish species (including 12 vulnerable species), and environmental features, at 1972 sites. The JSDM yielded more precise estimates of responses than single-species models fitted to each vulnerable species. Habitat associations inferred from the JSDM showed substantial overlap with those provided in COSEWIC status reports. Model-derived responses to environmental stressors can provide a management-friendly basis for species classification in terms of species’ tolerances to various forms of environmental change, and supplement the qualitative criteria for habitat requirements currently used in assessments of species vulnerability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".