Evaluating the role of data quality when sharing information in\n hierarchical multi-stock assessment models, with an application to Dover Sole
Bibliographic record
Abstract
An emerging approach to data-limited fisheries stock assessment uses\nhierarchical multi-stock assessment models to group stocks together, sharing\ninformation from data-rich to data-poor stocks. In this paper, we simulate\ndata-rich and data-poor fishery and survey data scenarios for a complex of\ndover sole stocks. Simulated data for individual stocks were used to compare\nestimation performance for single-stock and hierarchical multi-stock versions\nof a Schaefer production model. The single-stock and best performing\nmulti-stock models were then used in stock assessments for the real dover sole\ndata. Multi-stock models often had lower estimation errors than single-stock\nmodels when assessment data had low statistical power. Relative errors for\nproductivity and relative biomass parameters were lower for multi-stock\nassessment model configurations. In addition, multi-stock models that estimated\nhierarchical priors for survey catchability performed the best under data-poor\nscenarios. We conclude that hierarchical multi-stock assessment models are\nuseful for data-limited stocks and could provide a more flexible alternative to\ndata-pooling and catch only methods; however, these models are subject to\nnon-linear side-effects of parameter shrinkage. Therefore, we recommend testing\nhierarchical multi-stock models in closed-loop simulations before application\nto real fishery management systems.\n
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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 | 0.054 | 0.153 |
| 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 | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".