Subsurface and nighttime behaviour of pantropical spotted dolphins in Hawai'i
Bibliographic record
Abstract
Pantropical spotted dolphins (Stenella attenuata) are found in both pelagic waters and around oceanic islands. A variety of differences exist between populations in these types of areas, including average group sizes, extent of movements, and frequency of multi-species associations. Diving and nighttime behaviour of pantropical spotted dolphins were studied near the islands of Maui and Lana'i, Hawai'i, in 1999. Suction-cup-attached timedepth recorder/VHF-radio tags were deployed on six dolphins for a total of 29 h. Rates of movements of tagged dolphins were substantially lower than reported in pelagic waters. Average diving depths and durations were shallower and shorter than reported for other similar-sized odontocetes but were similar to those reported in a study of pantropical spotted dolphins in the pelagic waters of the eastern tropical Pacific. Dives (defined as >5 m deep) at night were deeper (mean = 57.0 m, SD = 23.5 m, n = 2 individuals, maximum depth 213 m) than during the day (mean = 12.8 m, SD = 2.1 m, n = 4 individuals, maximum depth 122 m), and swim velocity also increased after dark. These results, together with the series of deep dives recorded immediately after sunset, suggest that pantropical spotted dolphins around Hawai'i feed primarily at night on organisms associated with the deep-scattering layer as it rises up to the surface after dark.
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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".