A Bayesian hierarchical model of size-at-age in ocean-harvested stocks — quantifying effects of climate and temporal variability
Bibliographic record
Abstract
Quantifying size-at-age in harvested marine fish populations is a challenge with implications for both conservation and management. We describe a Bayesian hierarchical modeling approach for estimating mean and variation in size-at-age, incorporating environmental covariates. We apply the approach to two runs of Sacramento River Chinook salmon ( Oncorhynchus tshawytscha ): one data-rich (fall) and one data-poor (winter). We combine information on the size of recreationally harvested tagged fish and fishery size limits to reconstruct time-dependent marine size distributions. Our framework allows informed modeling of environmental effects on size-at-age, estimation of annual variability without overfitting, estimating size in years with limited data, and projecting sizes in future years. We found that fall run fish are anomalously small in years following Novembers with low values of the Northern Oscillation Index (NOI). Winter run data did not include anomalously low NOI years, but typical annual variability could be quantified. Importantly, our results suggest that it is possible to predict small size and slow growth during the upcoming fishery season on the basis of an environmental index available months in advance.
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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 | 0.008 | 0.012 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".