A Bayesian hierarchical meta-analysis of growth for the genus<i>Sebastes</i>in the eastern Pacific Ocean
Bibliographic record
Abstract
We conducted a meta-analysis of growth for 46 species of the genus Sebastes in the eastern Pacific Ocean using a Bayesian hierarchical model to estimate parameters, to investigate growth variability, and to elucidate meaningful biological covariates. Growth in terms of maximum attainable size (L∞) ranged from 12 to 80 cm, and instantaneous growth rates varied by over an order of magnitude (K; 0.03–0.34·year–1). Results from this method also confirm the theoretical, but often untested, view that growth parameters L∞and K are negatively correlated among populations or species of fish; Bayesian credibility intervals for correlation ranged from –0.2 to –0.7, with the posterior median of –0.4. The Bayesian hierarchical growth model showed less variability in growth parameters and lower correlations among parameters than those from standard techniques used in population ecology, suggesting that the absolute value of the correlation between L∞and K may be lower than the general perception in the ecological literature. Exploration of several covariates revealed that asymptotic size varied positively as a function of the size at 50% maturity. Finally, posterior probability distributions of the hyperparameters from this analysis provide plausible informative priors of growth for stock assessments of data-poor species.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".