An age-structured assessment model for chinook salmon (<i>Oncorhynchus tshawytscha</i>)
Bibliographic record
Abstract
Age-structured assessment models are rarely used for estimating the abundance of exploited salmon stocks. We developed such a model for a chinook salmon (Oncorhynchus tshawytscha) population in the Copper River, Alaska. Information consisted of catch-age data from three fisheries (commercial, recreational, and subsistence) and two sources of auxiliary data (escapement index and spawnerrecruit relationship). Model parameters included brood-year returns, proportions of a brood year returning at age and year, annual exploitation rates, gear selectivity, spawnerrecruit parameters, and a calibration parameter for the escapement index. Results suggested that population parameter estimates with high precision and low bias were produced by an approach that considered measurement error in the pooled catch-age data from all three fisheries and brood-year return proportions that varied over time. A sensitivity analysis revealed that brood-year return, catch, and escapement index estimates were insensitive to large changes in data weightings. The absence of strong deviations in the retrospective patterns of the brood-year returns suggested that there were no serious model misspecifications. The model integrated all sources of available information, accounted for uncertainty, and provided estimates of optimal escapement and its associated exploitation level. We believe that the model has broad application for use in assessments of chinook salmon systems.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".