A novel statistical method for validating the periodicity of vertebral growth band formation in elasmobranch fishes
Bibliographic record
Abstract
A simple age verification method is presented for centrum edge analysis (CEA) of elasmobranch species. In this method, a binomial model is linked with a von Mises distribution for circular data, taking the characteristics of the CEA data into account. The periodicity of growth band pairs is categorized as no cycle, an annual cycle, or a biannual cycle. Three models are then constructed according to different periodicities. We use the Akaike information criterion (AIC) to determine which model is the best. The models were applied to the shortfin mako shark ( Isurus oxyrinchus ) data collected in the North Pacific to identify the best periodicity model. The AIC best-fitting model was one involving an annual cycle. The general performance of the method was evaluated using simulated data of various sample sizes, formation times, and durations of growth band pairs. The simulation trials showed that the performance of the method was satisfactory with moderate sample sizes. This method should improve the accuracy of age determination and could be applied to all species that have periodic growth band pairs.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".