Effects of population-specific variation in age and length on fishery selection and exploitation rates of sockeye salmon (Oncorhynchus nerka)
Bibliographic record
Abstract
Conspecific salmonid populations often differ in age and body size at maturity, and these differences can cause fishery exploitation rates and patterns of directional selection on size to vary among populations. Based on age and length data on five representative spawning populations of sockeye salmon ( Oncorhynchus nerka ) in the Wood River system, Bristol Bay, Alaska, USA, we estimated exploitation rates and population-specific patterns of selection from a gillnet fishery between 1963 and 2007. Exploitation rates have differed among age groups and have varied greatly over time, likely due to changes in fishery selectivity. Populations with older, larger fish were more heavily exploited than populations with small, young fish. Differential fishery selection was detected among the populations, linked to persistent differences in size and age of fish among the populations. Specifically, we found evidence of stronger size selectivity on populations with smaller-bodied fish and less directional selection on populations dominated by larger-bodied fish. These results reveal the complex variation in the intensity and selectivity of fishing that can result from exploitation of populations that differ in age and size at maturity and shape. Evolutionary responses to this selection may be taking place, but natural processes provide countervailing selection.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".