A general model for salmon run reconstruction that accounts for interception and differences in availability to harvest
Bibliographic record
Abstract
Understanding population-specific spawner–recruit relationships is necessary for sustainable salmon management. Where multiple populations are harvested together, run reconstruction methods partition mixed-stock catches and allocate recruits back to their populations of origin. Traditional run reconstruction methods often use age composition data to inform catch partitioning. However age-only methods do not account for stock-specific differences in the availability of fish to harvest within fishing areas or the incidental harvest of nontarget stocks in nearby fishing areas. Advances in molecular genetic techniques permit genetic stock identification (GSI) of both contemporary and historical catch samples. We present a statistical model for salmon run reconstruction that utilizes both age composition and GSI data to estimate differences in the availability of stocks within, and interception rates among, terminal fisheries. When applied to the commercial sockeye salmon (Oncorhynchus nerka) fishery in Bristol Bay, Alaska, new estimates of population productivity differed from those generated using previous age-only methods by 0.1%–155.1%, with stock-specific mean absolute percent differences of 9.7%–38.7% across years, underscoring the value of genetic data for run reconstruction. With more accurate run reconstruction methods, spawner–recruit relationships can be identified more precisely, thus providing more accurate management targets for salmon 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".