Variations in righting behaviour across <scp>H</scp>ermann's tortoise populations
Bibliographic record
Abstract
Abstract In terrestrial animals with rigid protective structures, the ability to upright after being overturned can make the difference between life and death, especially in suboptimal thermal conditions or in the presence of predators. This trait is assumed to be under strong selection. Different factors can influence righting ability, body dimensions and body mass for instance. As these morphological traits diverge among populations, inter‐population variability in righting ability is expected. Previous studies on tortoises were performed within single populations and they usually focused on juveniles raised in captivity, precluding an assessment of the inter‐population variability in a natural (realistic) context. In the current study, we quantified the righting performance in four populations of free‐ranging adult tortoises. We found strong differences in righting success among populations and between genders, suggesting possible adaptations to local conditions. For instance, the topography (e.g. slopes) of each study site varied markedly. On average, males were more successful in righting themselves than females. Body size did not influence righting performances in males, but larger females were less successful compared to smaller ones. The success in righting was positively correlated with carapace domedness (height) and short bridges.
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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 | 0.001 |
| 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".