Behaviour and Physiology: The Thermal Strategy of Leatherback Turtles
Bibliographic record
Abstract
BACKGROUND: Adult leatherback turtles (Dermochelys coriacea) exhibit thermal gradients between their bodies and the environment of ≥8°C in sub-polar waters and ≤4°C in the tropics. There has been no direct evidence for thermoregulation in leatherbacks although modelling and morphological studies have given an indication of how thermoregulation may be achieved. METHODOLOGY/PRINCIPAL FINDINGS: We show for the first time that leatherbacks are indeed capable of thermoregulation from studies on juvenile leatherbacks of 16 and 37 kg. In cold water (< 25°C), flipper stroke frequency increased, heat loss through the plastron, carapace and flippers was minimized, and a positive thermal gradient of up to 2.3°C was maintained between body and environment. In warm water (25 - 31°C), turtles were inactive and heat loss through their plastron, carapace and flippers increased. The thermal gradient was minimized (0.5°C). Using a scaling model, we estimate that a 300 kg adult leatherback is able to maintain a maximum thermal gradient of 18.2°C in cold sub-polar waters. CONCLUSIONS/SIGNIFICANCE: In juvenile leatherbacks, heat gain is controlled behaviourally by increasing activity while heat flux is regulated physiologically, presumably by regulation of blood flow distribution. Hence, harnessing physiology and behaviour allows leatherbacks to keep warm while foraging in cold sub-polar waters and to prevent overheating in a tropical environment.
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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 | 0.000 | 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) | 0.001 | 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".