When can we reliably estimate the productivity of fish stocks?
Bibliographic record
Abstract
In modern fishery stock assessments, the productivity of exploited stocks is frequently summarized by a scale-invariant “steepness” parameter. This parameter, which describes the slope of the spawner–recruit curve, determines resilience of a stock to exploitation and is highly influential when estimating maximum sustainable yield. In this study, we examined conditions under which steepness can be estimated reliably. We applied a statistical catch-age model to data that were simulated over a broad range of stock characteristics and exploitation patterns and found that steepness is often estimated at its upper bound regardless of underlying productivity. The ability to estimate steepness reliably was most dependent on the true value of steepness, the exploitation history of the stock, natural mortality, duration of the time series, and quality of an index of abundance; this ability was relatively unaffected by levels of stochasticity in recruitment and sampling intensity of age compositions. We further explored the method of inverse prediction to improve estimates of steepness and conclude that this approach holds promise. We illustrate the utility of simulation and inverse prediction methods with two fish stocks located off the southeastern United States, greater amberjack ( Seriola dumerili ) and gag grouper ( Mycteroperca microlepsis ).
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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 | 0.018 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".