Evaluating the role of data quality when sharing information in hierarchical multistock assessment models, with an application to Dover sole
Bibliographic record
Abstract
An emerging approach to data-limited fisheries stock assessment uses hierarchical multistock assessment models to group stocks together, sharing information from data-rich to data-poor stocks. In this paper, we simulate data-rich and data-poor fishery and survey data scenarios for a complex of Dover sole (Microstomus pacificus) stocks. Simulated data for individual stocks were used to compare estimation performance for single-stock and hierarchical multistock versions of a Schaefer production model. The single-stock and best-performing multistock models were then used in stock assessments for the real Dover sole data. Multistock models often had lower estimation errors than single-stock models when assessment data had low statistical power. Relative errors for productivity and relative biomass parameters were lower for multistock assessment model configurations. In addition, multistock models that estimated hierarchical priors for survey catchability performed the best under data-poor scenarios. We conclude that hierarchical multistock 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 nonlinear side effects of parameter shrinkage. Therefore, we recommend testing hierarchical multistock 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 | 0.062 | 0.194 |
| 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".