Estimating growth from tagging data: an application to north‐east Atlantic tope shark <i>Galeorhinus galeus</i>
Bibliographic record
Abstract
This study addresses the inherent uncertainty when estimating growth from limited mark-recapture information. A selection procedure was developed utilizing 18 competing growth estimation methods. The optimal method for a given data set was identified by simulating the length at capture and recapture under different scenarios of measurement error and growth variability while considering the structure of observed data. This selection procedure was applied to mark-recapture data for 37 female and 16 male tope sharks Galeorhinus galeus obtained from tagging studies in the north-east Atlantic Ocean. Parameter estimates differed strongly among methods, showing the need for careful method selection. The selection approach suggested that best estimates for males and females were given by James' weighted least-squares approach with a fixed asymptote. Given an average total length (LT) at birth of 28 cm, the von Bertalanffy growth function of north-east Atlantic G. galeus would be LT = 200·85 - (200·85 - 28)e(-0·076t) for females and LT = 177·30 - (177·30 - 28)e(-0·081t) for males. The resulting age estimates were up to 11 years lower when compared with previous estimates derived from highly uncertain vertebrae readings. More generally, this procedure can help identify optimal estimation methods for a given data set and therefore aid in estimating more reliable growth parameters from mark-recapture information.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".