INTER-SEASONAL MAINTENANCE OF INDIVIDUAL NEST SITE PREFERENCES IN HAWKSBILL SEA TURTLES
Bibliographic record
Abstract
Within a single population of hawksbill sea turtles (Eretmochelys imbricata), we found a behavioral polymorphism for maternal nest site choice with respect to beach microhabitat characteristics. Some females preferred to nest in littoral forest and in places with overstory vegetation cover, and others preferred to nest in more open, deforested areas. Nest site choice was consistent within and between nesting seasons two years apart. This was not a result of females simply returning to the same location along the shoreline; beach sections used by individual turtles varied between seasons. Nest site choice was not influenced by changes in beach environment (e.g., beach width and foliage cover) or by changes in females' reproductive output (e.g., clutch size), suggesting that fidelity to particular microhabitats is a major determinant of the observed nesting patterns. Because hawksbills exhibit temperature-dependent sex determination, if the behavioral polymorphism in nest site choice has a genetic basis, as is plausible, then this would have implications for sex ratio evolution and offspring survival. By taking an individual-based approach to the study of maternal behavior we reveal previously overlooked individual variation and hope to provide some impetus for more detailed studies of nest site choice.
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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 | 0.001 |
| 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".