Sea-level index of recruitment variability improves assessment model performance for sablefish <i>Anoplopoma fimbria</i>
Bibliographic record
Abstract
Environmental recruitment indices may improve the precision of stock assessments, allow hindcasting, and aid in near-term forecasting. We used Bayesian dynamic factor analysis (DFA) to find common trends in sea level from 16 tide gauges spanning the US West Coast. We then used these dynamic factors as predictors of sablefish Anoplopoma fimbria recruitment deviations from the 2021 assessment. We evaluated the ability of the resulting northern sea-level index (north of Cape Mendocino, ∼40°N) to inform recruitment estimates and its impacts on assessment model predictions by running two hindcast stock assessment models: (1) a catch-only model, which assumed average recruitment from the stock–recruit relationship, and (2) a catch plus sea-level model. In both cases, survey data were removed from 2011 forward. The model including sea-level index captured the observed increase in stock biomass from 2016 onwards, while the catch-only model did not, predicting a continued biomass decline. This work provides evidence of the potential to improve forward-looking stock projections by better capturing stock trends, providing an advance over average recruitment assumptions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".