Including mark–recapture data into a spatial age-structured model: walleye pollock (<i>Theragra chalcogramma</i>) in the eastern Bering Sea
Bibliographic record
Abstract
Integrated assessment models used to evaluate fish stocks are becoming increasingly complex, with some capable of incorporating spatial considerations. Such a model has been developed to estimate movement of walleye pollock (Theragra chalcogramma) between the northwestern and southeastern eastern Bering Sea. In this study, we investigate the feasibility of estimating movement using spatially disaggregated data supplemented by tagging data. Monte Carlo simulation was used to test accuracy and variability of parameter estimation in model scenarios with and without tagging information. Total biomass estimates for models with and without tagging data were unbiased, but uncertainty was smaller when tagging data were available. Uncertainty was also reduced in regional biomass and movement parameters when including tagging data. Our findings indicate that tagging information would be important to provide reliable spatially explicit fisheries management advice for eastern Bering Sea pollock.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".