High‐resolution diving data collected from foraging area reveal that leatherback turtles dive faster to forage longer
Bibliographic record
Abstract
Abstract Despite multiple studies examining the diving behavior of leatherback sea turtles (Dermochelys coriacea) at coarse resolution over broad distances, there is still a paucity of high‐resolution diving data collected in areas where foraging has been confirmed. Short‐term (~1–3 h) deployments of suction cup tags with time–depth recorders (TDRs) on 10 free‐swimming leatherback turtles in a foraging area off Nova Scotia, Canada during August and September (2007–2014), captured a total of 161 dives. High‐resolution (1–5 s sampling rate) dive profile data indicated variability in diving behavior between and within individuals. On average, turtles spent 55.7% of their time diving and 44.3% at the surface. Turtles generally performed short (mean duration = 250.4 s [SD = 47.9 s]) and shallow dives (mean depth = 24.3 m [SD = 5.8 m]). We recorded a mean dive descent rate of 0.32 m/s, which is faster than values recorded for leatherbacks in tropical waters. This may reflect differences in environment, behavioral mode (e.g., foraging vs. inter‐nesting), and body condition. Linear mixed‐effects models suggest a significant positive correlation between descent rate and mean depth, maximum depth, and integrated vertical bottom movement (IVBM). Turtles with faster descent rates dove deeper and increased their predicted foraging behavior (IVBM, or the sum of absolute differences in depth changes while at the bottom portion of their dives). Models additionally showed that dive time, bottom time, and IVBM were all positively correlated to the post‐dive surfacing. This suggests that turtles required more time at the surface to recover and/or handle prey following longer dives characterized by increased vertical movement at the bottom portion of the dive. Dives were complex; the application of standard dive type/shape analysis may be over‐simplified and inappropriate for leatherbacks foraging in these habitats. These results portray a novel and detailed look at the foraging dynamics of a diving marine reptile.
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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) | 0.002 | 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".