Mating patterns of dusky dolphins (<i>Lagenorhynchus obscurus</i>) explored using an unmanned aerial vehicle
Bibliographic record
Abstract
Abstract Few studies have explored the mating patterns of free‐ranging cetaceans, largely because of logistical challenges. We used an unmanned aerial vehicle (UAV) to follow and video‐record 25 groups of mating dusky dolphins ( Lagenorhynchus obscurus ) near the surface of the water and examine how behavior patterns varied with mating group type. We collected aerial footage of dolphins mating in traditional Isolated Pods and within Integrated Pods and compared differences in the number of mating animals, swimming speed, bearing change, percent time at the surface of the water, female respiration rate, copulatory position rate, and sex‐specific mating behaviors. Only the mean number of mating animals and some sex‐specific mating behaviors varied significantly between the two mating group types. More dolphins were engaged in mating behaviors in Isolated Pods than Integrated Pods. Males engaged in more interference behaviors in Isolated Pods compared to Integrated Pods. Females performed fewer speed bursts but more rolls on their backs in Integrated Pods compared to Isolated Pods. Several similarities and differences were found in comparison to boat‐based research of the same population of dolphins. We highlight the value of UAVs for noninvasive and accurate collection of cetacean behavioral data.
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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 | 0.000 |
| 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".