Living on the edge: Traits of freshwater fish species at risk in Canada
Bibliographic record
Abstract
Abstract The native ranges of many species in North America reach their northern extent in southern Canada, which results in several aquatic species with core populations found farther south being assessed as at risk by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) and receiving protection under the Species at Risk Act (SARA). To determine traits that predict at‐risk status for freshwater fishes in Canada a classification and regression tree analysis was performed using a suite of ecological and life‐history traits, and the species’ distributions in Canada. Range‐edge distribution in Canada was a significant predictor of a species assessed as at risk by COSEWIC and to be listed as at risk under SARA. Other predictive traits included Balon reproductive guild, reproductive age/maximum age ratio, and lifespan. Species with economic value were also not likely to be assessed as at risk by COSEWIC. Analyses showed greater inconsistency in listing status under SARA than COSEWIC assessment, and a bias toward not listing species, despite predicted at‐risk status, was evident. The predictive models may prove useful in making future conservation decisions and highlight species that should have their status (re)assessed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".