Reducing Gill-Net Mortality of Incidentally Caught Coho Salmon
Bibliographic record
Abstract
The abundance of coho salmon Oncorhynchus kisutch has declined dramatically over much of the southern part of its range along the Pacific Coast of North America. This decline has created the need to reduce fishing mortalities, including bycatch mortalities in fisheries that are targeting other species. Traditional gill-net fishing causes an estimated 35–70% mortality rate on incidentally caught coho salmon. A reduction in this high mortality rate is necessary if gill nets continue to be used in fisheries that inadvertently intercept depressed coho salmon stocks while fishing other species. By using modified gear, short net soak times, careful handling of fish on removal from the gill net, and a newly designed recovery box, the short-term mortality rate on incidentally caught coho salmon can be reduced to as little as 6%, possibly even lower in some circumstances. This substantial reduction in mortality on nontargeted species expands the possible role of gill nets in the development of selective fisheries.
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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 | 0.000 | 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".