Bibliographic record
Abstract
A general model is developed for salmon run reconstruction based on catch, escapement, and age composition data. The model is based on “groups” of salmon, each of which share the same characteristics but can differ from other groups in run timing, abundance, gear selectivity, and migration routes. The model is highly flexible so that it can be adapted to a variety of fisheries and can compare the fits of alternative hypotheses to available data. The model is applied to three sockeye salmon ( Oncorhynchus nerka ) districts in Bristol Bay, Alaska, USA, to show the effect of allowing age classes to arrive at different times and the impact of including process errors to mimic day-to-day arrival variability. The model predicts that in 2005, Wood River salmon comprised only 54% of the catch in the Nushagak fishing district (but 71% of the escapement), although these predictions are contradicted by genetic data for 2006–2008 showing high harvest rates of Wood River and Nushagak River fish but only light harvest rates of Igushik River fish. The genetics highlight the importance of including stock-specific availability parameters in future versions of the model to account for differences in harvest rates among stocks caught in the same fishing district.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".