Estimating impacts of the pollock fishery bycatch on western Alaska Chinook salmon
Bibliographic record
Abstract
Chinook salmon (Oncorhynchus tshawytscha) are taken as bycatch in the Bering Sea pollock (Gadus chalcogrammus) fishery, with recently revised management measures in place to limit the overall Chinook salmon catch.Historical impact of the bycatch on regional salmon stocks is made difficult because, until recently, sampling for the stock composition of the bycatch was patchy and diverse in approaches.In this study, extensive observer data on the biological attributes (size and age composition) of the bycatch were used to estimate the impact on specific regional stock groups (RSGs), as defined given available genetic stock identification estimates.Our model provides estimates of the impact on Chinook salmon RSGs, given seasonal and spatial variability in the bycatch, and accounts for observed in-river age compositions, uncertainty in age-specific oceanic natural mortality of Chinook salmon, and between-year variability in genetic information.The upper Yukon River stock is transboundary and subject to heightened management interest and international management agreements on escapement goals.Our study updates results from an earlier analysis used to develop the management regulations that went into place in 2011.It shows that the new data result in slight changes in previous estimates, and that the lower overall Chinook salmon bycatch since 2008 has resulted in lower impacts to the main western Alaskan RSGs.
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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) | 0.001 | 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 | 0.001 |
| 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".