Australian snubfin vocal activity is influenced by behavioral state and group characteristics
Bibliographic record
Abstract
Abstract Acoustic communication is an important aspect of life for marine mammals, as their environment often limits the reliability of visual cues. However, there is little information regarding the acoustic communication and behavior of Australian snubfin dolphins (Orcaella heinsohni). This study was designed to determine if call rate and type were significantly affected by the behavioral state, group size, and cohesion of snubfin dolphins in the Fitzroy River in Queensland, Australia. We found that dolphins significantly modified both call rate (calls/hour/individual) and call type among behavioral states. For example, call rates were higher when dolphins were foraging versus resting or traveling. We also found that group size and cohesion had minimal effects on call rate, but significantly affected the predicted probabilities of call type production. For example, the probability ratio of burst pulse to whistle production is estimated to be highest when groups are widespread (>10 m), indicating the potential importance of burst pulses in maintaining contact between dispersed individuals. This study presents the first comprehensive analysis of snubfin dolphin communication under natural noise conditions in relation to behavioral context, which provides a foundation to explore how anthropogenic acoustic masking and behavioral disturbances may affect these dolphins in the future.
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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 | 0.000 | 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".