Visible Gill‐Net Injuries Predict Migration and Spawning Failure in Adult Sockeye Salmon
Bibliographic record
Abstract
Abstract Fish that survive nontake fisheries interactions may subsequently die, a phenomenon that is generically termed “fisheries‐related incidental mortality” (FRIM). Gill nets, which typically asphyxiate fish and visibly damage their integument, inflict higher rates of FRIM than other commonly used gears. To better define FRIM associated with gill‐net encounters, an observational study coupled with biotelemetry measured migration survival and spawning success of a Sockeye Salmon Oncorhynchus nerka population during the final 45 km of their freshwater spawning migration (in 2014, 2015, and 2016). The daily prevalence of gill‐net injuries ranged from 0% to 80% of fish, resulting in an annual prevalence of 21–29% for females and 13–22% for males (over 3 years). Fish with visible gill‐net wounds had a 16% lower probability of completing their migration, and female fish with gill‐net wounds had an 18% lower probability of successfully spawning. As a result, the annual proportion of effective female spawners that died in the final 45 km of their migration due to gill‐net injuries was estimated to range from 3.8% to 9.9% (500 to 1,600 females). In addition, stray Sockeye Salmon from upriver populations commonly died at the tagging site, and visible gill‐net injuries were observed in half of those fish during a year with high mortality. If wild salmon populations continue to decline as climate change progresses, fisheries managers will be under greater pressure to minimize FRIM.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".