The promises and pitfalls of including decadal-scale climate forcing of recruitment in groundfish stock assessment
Bibliographic record
Abstract
Concurrent declines in demersal fish stock abundances and shifts in long-term average environmental conditions have been well documented in the Pacific. Management advice ignoring environmental forcing of recruitment may cause stocks to be over- or under-harvested, so it is important to consider including environmental forcing on recruitment in stock assessment models. Simulation testing is used to determine the statistical power of stock assessments to identify long-term, decadal-scale environmental forcing of recruitment and the ability to estimate management reference points when the duration of the fisheries time series is equal to or less than the period of the environmental cycle. Commonly used assessment methods generally lead to lower total type I (incorrectly rejecting the null hypothesis of no environmental impact) and type II (failing to detect an environmental impact when such a relationship exists) error rates. The promise of integrating environmental data with decadal-scale variability directly into stock assessments via the stock–recruitment relationship is outweighed by the pitfall of high type I error rates that are due to fishing-induced stock declines that coincide with directional environmental change. However, the impact of type I errors can be minimized by choosing an appropriate combination of assessment method and reference point estimators.
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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 | 0.059 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".