Parameterizing age-structured models from a fisheries management perspective
Bibliographic record
Abstract
Age-structured models are widely used in fisheries stock assessments and contain two very important parameters that determine the rate and amount of harvest that can be safely taken: the compensation rate in juvenile survival (κ) and the unfished biomass (Bo). These two parameters are often confounded. It is common for relative abundance indices to lack contrast, and the use of informative priors, or fixing at least one of these parameters, is necessary to develop management advice. Providing management advice proceeds by transforming estimates of biological variables such as Boand κ into management variables such as the maximum sustainable yield (C*) and the fishing mortality rate that would achieve this yield (F*). There is no analytical solution for the transformation of Bo, κ to C*, or F* for age-structured models with commonly used stock–recruitment functions and therefore must be done numerically. The opposite transition, however, does have an analytical solution for both the Beverton–Holt and Ricker recruitment models with partial selectivities for all age classes. Use of these analytical solutions allows for age-structured assessment models to be directly parameterized in terms of the management variables C* and F*. The effects of informative priors on C* and F* on the results of the assessment model are completely transparent to management.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".