Bayesian estimation of von Bertalanffy growth parameters for gray triggerfish, <i>Balistes capriscus</i>, incorporating multiple readers and ageing structures
Bibliographic record
Abstract
Ageing error can significantly impact growth parameter estimates. However, ageing error is rarely considered as a source of uncertainty as growth is generally estimated from a single age reader's age estimates. Here, we developed a sex-specific Bayesian hierarchical formulation of the von Bertalanffy growth model (VBGM) to estimate gray triggerfish ( Balistes capriscus) growth parameters from multiple ( n = 3) reader age estimates derived from otolith opaque zone counts, a new dorsal spine protocol, and the historical (old) dorsal spine protocol. For all three ageing protocols, estimates of length-at-age were significantly different between sexes, resulting in significantly higher estimates of L∞ for males compared to females. Estimates of the Brody growth coefficient ( k) from both the otolith and new spine ageing protocols were significantly lower than the old spine protocol estimate. Study results provide a framework for comparing VBGMs, suggest it is most appropriate to fit sex-specific growth models for this species, and suggest stock assessments using ages estimated with the old spine ageing protocol would produce biased results.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".