Spatiotemporal index standardization improves the stock assessment of northern shrimp in the Gulf of Maine
Bibliographic record
Abstract
Estimated trends in relative stock abundance are a primary input to fish stock assessments. Accurate and precise estimates are essential for successful conservation and management. Scientifically designed data collection ensures that estimates of relative abundance are unbiased. However, the statistical efficiency of a design-based estimator may be low under certain circumstances. We apply a recently developed spatiotemporal model that incorporates habitat variables to estimate a model-based abundance index for northern shrimp (Pandalus borealis) in the Gulf of Maine. We contrast this spatiotemporal index with a classical design-based index and evaluate the impacts of differences between the two abundance indices on the stock assessment. We show that using the spatiotemporal index in the assessment model greatly alters the estimates of recruitment and spawning stock biomass and the determination of stock status. Also, incorporating the spatiotemporal index leads to less retrospective bias and outperforms the model with design-based index in terms of predictive performance through a retrospective cross-validation test. Our results suggest that temporal variability of population abundance could be exaggerated by the design-based estimator, and such imprecision may greatly affect the performance of a stock assessment and subsequent development of management decisions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".