A closed-loop simulation framework and indicator approach for evaluating impacts of retrospective patterns in stock assessments
Bibliographic record
Abstract
Abstract A retrospective pattern within a stock assessment occurs when historical estimates systematically increase or decrease as data are removed and has been cited as a cause of persistent overfishing. For two case studies, Gulf of Maine cod and New England pollock, we demonstrated how closed-loop simulation can be used to evaluate the impacts of retrospective patterns with respect to management objectives. Operating models (OM) representing alternative states of nature were developed and various management procedures (MP) that account for retrospective patterns in the fitted assessment models were applied. From the cod example, downward adjustment of the catch advice based on Mohn's rho was more beneficial over model averaging (MA) to meeting biomass objectives from the cod example and avoiding stock crashes. For pollock, downward adjustment and MA were robust to meeting biomass objectives at the cost of foregone catch. The ability to discriminate OMs, using indicators generated from the simulated projections, varied by MP and time for cod, but was poorer overall for pollock. This framework could be used to identify if retrospective issues generate poor management outcomes and, in some cases, alleviate pressure to identify the single most credible state of nature.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".