Modeling of age-dependent natural mortality rates for long-lived fishes based on the Richards model family
Bibliographic record
Abstract
Natural mortality rates ( M) are poorly quantified and commonly identified as a key source of uncertainty in fish stock assessments. Increasingly, stock assessments account for size- and age-dependence in M instead of using a constant M for all sizes and ages, as has traditionally been the default assumption. Empirical studies show that M is approximately inversely proportional to body length in fish populations, and this generalization has been used together with a von Bertalanffy growth curve to derive an age-dependent M. Here we extend this approach to the three commonly used growth functions of the Richards family (i.e., Logistic, von Bertalanffy, and Gompertz growth models). These models allow flexible production of various growth curves with a sigmoid shape and an upper asymptote, making them useful for displaying diverse growth curves for various long-lived fishes. The influences of growth parameters on predicted M were investigated, and the outcomes were compared among the three growth models. Age-dependent M for blue sharks ( Prionace glauca) were estimated as an example.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".