Population-specific mortality in coho salmon (Oncorhynchus kisutch) released from a purse seine fishery
Bibliographic record
Abstract
Abstract Coastal commercial fisheries targeting Pacific salmon inevitably capture a mix of co-migrating species and genetically distinct populations within each species, only some of which are sufficiently abundant to sustain exploitation. Species-specific release measures are implemented as a conservation measure, but there remains little understanding of the resulting mortality. A purse seine fishery for Pacific salmon in British Columbia, Canada, was simulated with the goal of estimating post-release mortality for coho salmon, a species commonly released from commercial fisheries. Landed coho salmon (n = 220) were tagged with acoustic transmitters and tracked along their coastal approach and into freshwater. Survival analyses accounting for variable migration pathways among populations revealed population-specific survival, with the population of greatest conservation concern having the best survival. Condition assessments revealed scale loss to be the strongest predictor of success. Physically exhausted fish, identified via reflex impairment tests, also experienced higher mortality. Results highlight the complexity of estimating release mortality in mixed-population commercial fisheries and are discussed in the context of management implications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".