Validation of a Rapid Visual-Assessment Technique for Categorizing the Body Condition of Green Turtles (Chelonia mydas) in the Field
Why this work is in the frame
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Bibliographic record
Abstract
Many important questions in ecology and conservation biology require assessment of the body condition of animals, which is often achieved using mass and length data. However, fully quantitative condition indices can be difficult to obtain in the field for large taxa like marine turtles. Therefore, rapid visual-assessment techniques for categorizing condition can be useful for field studies. Here, we test whether a visual method of categorizing body condition in the Green Turtle (Chelonia mydas) based on the shape of a turtle's plastron is comparable to two commonly used body condition indices derived from mass and length measurements. Condition scores for both mass–length indices varied in the expected manner with our visual condition categories, verifying that the rapid visual assessment technique accurately reflects differences in body condition. This technique should aid many field studies of turtles where body condition data are required but mass data cannot easily be obtained.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it