Estimation of age composition from length data by posterior probabilities based on a previous growth curve: application to <i>Sebastes schlegelii</i>
Bibliographic record
Abstract
We developed a system to estimate the age composition of a fish population (Sebastes schlegelii) from length data by considering fish growth, length variation, proportion of age classes, and sexual dimorphism. Reasonable interpolations allowed age composition to be estimated when length data and agelength relationships were measured in different seasons. A growth curve was fitted to the mean length growth using a maximum likelihood method with an assumption of a normal distribution in length variation. Posterior probabilities were constructed with normal distributions according to Bayes’ theorem, and age composition and its confidence limits were reasonably estimated from the posterior probabilities and by bootstrap resampling. The influence of annual fluctuations of population properties was assessed by cross-validation, which was improved by updating the prior probabilities. While the new system was more robust than the agelength key for small numbers of aging data, it was impossible to improve the system by focusing on the length data alone because the correlation between the estimation error and the likelihood calculated from the length data alone was weak.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".