Analytical expressions of sustainable benchmarks generic for all two-stage-structured models of exploited fish populations
Bibliographic record
Abstract
Of the types of fishery models that use time series of removals, fishing effort, and indices of abundance, two-stage-structured models are less prevalent, especially in developing the maximum sustainable yield (MSY) benchmarks, in part because of ignorance of how to calculate those benchmarks. To fill this gap, this study aims to derive analytical expressions for calculating MSY benchmarks associated with a continuous delay-differential model, given that the recruit stage relates to the spawning-stock biomass (SSB) according to a stock–recruit relationship (SRR) and that the SRR parameters are combined with the components of the composite (two-stage-structured or age-aggregated) yield per-recruit model CYPR14. The focus is on analytical expressions of the fishing mortality producing MSY ( FMSY), considering that the equilibrium yield comes from either the equilibrium SSB at the start of fishing regimes or the equilibrium average SSB during fishing regimes. The expressions obtained are versatile for all two-stage-structured models. Derivations employing the fished equilibrium average SSB are recommended for use because they produce precautionary MSY benchmarks and because they are consistent with their counterparts in contemporary age-structured stock assessment models.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".