Gill-Net Fishing Effort Predicts Physical Injuries on Sockeye Salmon Captured near Spawning Grounds
Bibliographic record
Abstract
Abstract Nonretention in gill-net fisheries for Pacific salmon Oncorhynchus spp. can be relatively high and can cause a variety of impairments to nonretained fish, which often lead to immediate or delayed mortality. We sought to improve the understanding of the association between gill-net escapement and injuries incurred by upriver-migrating salmon by examining the relationship between gill-net fishing effort in the Fraser River, British Columbia, and the frequency and severity of gill-net injuries to migrating Sockeye Salmon Oncorhynchus nerka. Adult Sockeye Salmon were intercepted at a location approximately 335 km from the mouth of the Fraser River and assessed for gill-net injuries. Gill-net fisheries targeting Sockeye Salmon operated throughout the first 320 km of the Fraser River main stem starting at the mouth of the river. A generalized linear mixed model was used to identify the role of gill-net fishing effort, fork length, and sex on the probability of an individual fish sustaining a gill-net injury. Predicted probabilities of gill-net injury ranged from 12% to 46% across all levels of fishing effort, suggesting that gill-net injuries were more prevalent among individuals that encountered high levels of fishing effort. However, fishing effort did not seem to influence the severity of gill-net injuries. Our results suggest that estimates of fishing effort may be useful in predicting the probability of gill-net injury to migrating fish, which could help managers estimate en route mortality and more accurately predict spawner escapement.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".