Large-scale investment in the excavation and “camouflaging” phases by nesting Leatherback Turtles (<i>Dermochelys coriacea</i>)
Bibliographic record
Abstract
Nesting female Leatherback Turtles (Dermochelys coriacea (Vandelli, 1761)) are ungainly and vulnerable to predation, stranding, and physiological stress, dangers encountered repeatedly in a nesting season. Minimising these risks must be balanced against optimising investment in nest-site selection, excavation, refilling, and concealment of their egg clutches. We investigated the relative investment in the different phases of nesting in Leatherback Turtles by recording the duration of, and respiratory frequency during, each phase. Nest excavation and the final sand scattering (usually termed “camouflage” or “disguise”) phases were the longest in duration. By combining the relative durations of nesting phases with literature estimates of oxygen consumption, we found that these may be the most expensive phases energetically, sand scattering in particular. Leatherback Turtles follow convoluted paths during the sand scattering phase, which were mapped and measured. They disturb large areas (mean (±SD) 30.3 ± 11.1 m2, maximum 57 m2), the centroids of which were always offset from the nest itself. Interestingly, in none of 26 nestings observed did the centre line of females move over the nest itself. Nesting Leatherbacks therefore invest heavily in obscuring the egg clutch, and the pattern of their movements in doing so suggests decoy behaviour.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".