Influence of pinniped-caused injuries on the survival of adult Chinook salmon (<i>Oncorhynchus tshawytscha</i>) and steelhead trout (<i>Oncorhynchus mykiss</i>) in the Columbia River basin
Bibliographic record
Abstract
Increasing pinniped abundance in the Pacific Northwest has coincided with population declines of Pacific salmon ( Oncorhynchus spp.) and steelhead trout ( Oncorhynchus mykiss ), and concentrated predation may affect the recovery of some threatened and endangered salmonid stocks. We used radiotelemetry to evaluate pinniped-caused injury effects on migration survival of 17 007 adult Columbia River Chinook salmon ( Oncorhynchus tshawytscha ) and steelhead trout. Injuries from pinnipeds were common (mean injury rate across 29 run-years = 36.5%) and were most common for spring Chinook salmon and steelhead trout. Injury was not consistently associated with adult survival to spawning tributaries, but some negative survival effects were detected. Pinniped-caused injury rates decreased as annual run sizes increased, indicating density-dependent or saturation effects. Within a run, large fish generally had a higher injury incidence than small fish, suggesting pinnipeds targeted large fish or more efficiently captured small fish. Seasonal, size-dependent, and density-dependent results imply that pinniped effects likely differ widely among salmonid populations within the Columbia River basin. A better understanding of these effects is needed to guide management and conservation strategies.
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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".