Bibliographic record
Abstract
We compared the ability of four length-frequency analysis programs to generate accurate von Bertalanffy growth parameters for a population of green turtles (Chelonia mydas) of known growth rates. The four programs were ELEFAN I, Shepherd's length composition analysis (SLCA), projection matrix method, and MULTIFAN. ELEFAN failed to identify a set of parameters that qualified as a best fit. The parameter estimates generated by SLCA successfully described six of the 10 length distributions from the population of green turtles. The parameter estimates produced by the projection matrix method failed to describe adequately any of the 10 length distributions. MULTIFAN generated a set of growth parameter estimates that successfully described all of the 10 length distributions. Although MULTIFAN had the best performance, it requires substantially more initial information and estimates than do the other programs. The best approach-particularly with a poorly studied population-may be to conduct initial analyses with SLCA, followed by analyses with MULTIFAN. Length-frequency analysis is a useful method for the study of growth in populations of immature sea turtles. Further study is required to determine whether these methods are appropriate for populations of sea turtles that include mature individuals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.983 | 0.982 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".