Spatiotemporal model improves survey indices for witch flounder stock assessment in the Grand Banks
Bibliographic record
Abstract
Accurate and precise estimates of survey abundance indices are essential inputs for stock assessment models and are important for the successful conservation and management of fisheries stocks. Since abundance indices can be standardized in various different ways, from conventional design-based approaches to model-based approaches, it is essential to compare the efficiency of those approaches and quantify the consequences for stock assessments. In this study, we focus on an important commercial stock of witch flounder (Glyptocephalus cynoglossus) in NAFO 3N+3O divisions. We first compare the survey indices standardized by design-based and model-based approaches, and find that a model-based estimator provides more precise estimates of survey indices than design-based approach for this stock. We then apply a widely used age-structured statistical catch-at-length (ACL) assessment model to the standardized catch-at-length survey indices from both approaches to estimate the age-based population dynamics for this stock. We conclude that the ACL model fit to model-based indices performed better than the same model fit to design-based indices.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".