Comparing a suite of surplus-production-based stock status identification approaches and management procedures
Bibliographic record
Abstract
Different approaches have been used to identify fishery stock status when only biomass and catch data are available. However, the performance of the approaches may be affected by the uncertainties derived from different sources (e.g., model misspecification, stock productivity changing, observation error). Here, we propose that the observed biomass associated with the highest calculated surplus production can be used as an indicator (Bhighest_S) to identify stock status. We develop a management procedure (MP) atop a widely used method (i.e., Gcontrol) by incorporating Bhighest_S in the harvest control rule. Two simulations are conducted to compare the stock status identification approaches and corresponding MPs. Using Bhighest_S to identify stock status performs better than surplus production modeling approaches in simulated regime shift scenarios. Compared with the old version of Gcontrol, incorporating Bhighest_S or estimated BMSY in the harvest control rule provides more stable and higher yields. This study contributes to the development and evaluation of indicator-based stock status identification approaches and MPs that only require biomass and catch data.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".