Model averaging to estimate rebuilding targets for overfished stocks
Bibliographic record
Abstract
Reducing overfishing and recovering overfished fish stocks is a challenging and important global problem. Rebuilding targets are essential ingredients for guiding overfished stocks to recovery. Having robust estimates of rebuilding targets is likely a necessary condition for effective long-term management of fishery resources. In this paper, we show how Bayesian model averaging can be applied to estimate rebuilding targets under alternative hypotheses about stockrecruitment dynamics. Using alternative hypotheses about stockrecruitment dynamics and environmental variation is important because using only a single hypothesis can lead to substantially different reference points and policy implications. The alternative hypotheses are augmented with prior information collected from meta-analyses of stockrecruitment data to construct a set of age-structured production models. We illustrate our approach using three overfished New England groundfish stocks. We fit alternative model likelihoods to observed data using Bayesian inference techniques. The Schwarz goodness-of-fit criterion was used to calculate model probabilities. Bayesian model averaging was used to estimate rebuilding targets that were robust to model selection uncertainty. Model-averaged estimates suggested that rebuilding targets for overfished stocks can be reasonably well determined if adequate prior information on stock productivity is available. Nonetheless, results had wide confidence intervals that reflected the underlying uncertainty in rebuilding targets.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".