Evaluating the role of data quality when sharing information in hierarchical multi-stock assessments, with an application to dover sole
Bibliographic record
Abstract
An emerging approach to data-limited fisheries stock assessment uses hierarchical multi-stock assessment models to group stocks, sharing information from data-rich to data-poor stocks. In this paper, we simulated data-rich and data-poor scenarios for a complex of dover sole. Simulated data for individual stocks were used to compare estimation performance for single-stock and hierarchical multi-stock Schaefer production model configurations. The single-stock and best performing multi-stock models were then used in stock assessments for the real dover sole data. Multi-stock models often had lower estimation errors than single-stock models when assessment data were of poor quality. Relative errors for productivity and relative biomass parameters were lower for multi-stock assessment models, and multi-stock models that estimated hierarchical priors for catchability performed the best under data-poor scenarios. We conclude that hierarchical multi-stock assessment models are useful for data-limited stocks and could provide a more flexible alternative to data-pooling and catch only methods; however, these models are subject to non-linear side-effects of shrinkage. Therefore, we recommend testing hierarchical multi-stock models in closed-loop simulations before application to real fishery management 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 | 0.091 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.003 |
| 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".