Inferring prior distributions of recruitment compensation metrics from life-history parameters and allometries
Bibliographic record
Abstract
The recruitment compensation metrics, the stock–recruit steepness (h) and compensation ratio (κ), are difficult to estimate, yet they are integral components of contemporary fishery models. To aid in the estimation of κ and h, a hybrid method to construct their prior distributions for a species is developed. The method is hybrid because it integrates (i) a meta-analysis of the relationship between maximum rates of recruit production (α) and asymptotic lengths obtained across fish species of different life histories and (ii) species-specific unfished spawning biomass per recruit (Φ0). This method is applied to five finfish species found off the East Coast of the United States. Uncertainty is introduced by sampling growth parameters, α, and natural mortality and — through Monte Carlo simulations — propagated into Φ0, κ, and h for the Beverton–Holt and Ricker stock–recruit relationships. Descriptive statistics and parameters from probability density functions of the simulated distributions of κ and h are generated. The method developed requires fewer inputs than the reproductive ecology method while likewise allowing the development of species-specific statistics for κ and h. These statistics, rather than their counterparts generated from classical meta-analyses, are better suited for use in fishery 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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".