The estimation and robustness of <i>F</i><sub>MSY</sub> and alternative fishing mortality reference points associated with high long-term yield
Bibliographic record
Abstract
Equations for equilibrium yield and biomass are presented and used to derive FMSY and alternative reference points (ARPs). ARPs are analogues of the traditional points based on yield-per-recruit (YPR) or spawning stock-per-recruit (SPR) (F0.1, F40%, F50%), but refer to the equilibrium total yield or biomass. The method combines YPR and SPR analysis with stock–recruitment relationships. The sensitivity of FMSY and ARPs to the range of available stock–recruitment data, recruitment variance, various steepness levels in the stock–recruitment models, assessment variance, and bias are tested. The analysis showed that in most cases, F40%B and F50%B, defined by the equilibrium biomass (B), were the most robust relative to the different sources of uncertainty. However, in the case of the misspecification of the stock–recruitment relationship, FMSY showed superior performance. F40%B for the Beverton and Holt stock–recruitment model and F50%B for the Ricker recruitment model can be recommended as conservative fishing mortalities associated with high long-term yield. For the considered steepness, they produced yield up to 5%–10% lower than yield at FMSY.
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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 | 0.014 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".