Water mass characteristics and solar illumination influence leatherback turtle dive patterns at high latitudes
Bibliographic record
Abstract
Eastern Canada hosts one of the largest seasonal aggregations of leatherback turtles (Dermochelys coriacea) in the Atlantic Ocean, and is considered critical foraging habitat. Explaining distributional variation of leatherbacks in this three‐dimensional habitat is relevant to the recovery strategy for this endangered species as human activities are a leading cause of mortality. We identify high‐resolution spatial and temporal patterns in leatherback movements, and associated environmental variables shaping leatherback habitat use in Atlantic Canadian foraging waters. Data loggers were deployed on three female leatherbacks off Halifax, Nova Scotia, and were recovered during subsequent nesting in South and Central America. Time (0.5 Hz), depth (±1 m), water temperature (±0.1°C), and location data were recorded and analyzed for the period when the turtles were resident in their Canadian foraging domain. We demonstrate that leatherback dives are primarily restricted to the main thermocline, suggesting a food‐related water mass association. We also identified low‐ and high‐frequency periodicities in turtle depth‐at‐time, reflecting a diel pattern triggered by nautical twilight and dive periods of 8–10 minutes. We further demonstrate that dive frequency is a function of seasonal change in daylight. Our findings illustrate that solar illumination influences leatherback diving in north temperate waters, consistent with turtles using visual cues for foraging.
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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".