Behavioural responses of Irrawaddy dolphins (Orcaella brevirostris) to a dead conspecific
Bibliographic record
Abstract
Cetacean behaviour has long attracted scientific attention as humans endeavour to discover what makes these mammals so emotive and engaging. To date, much of this research has focussed on abundant and widely distributed cetacean species such as bottlenose dolphins ( Tursiops truncatus ) and humpback whales ( Megaptera novaeangliae ). As an endangered and often evasive species, research regarding Irrawaddy dolphin ( Orcaella brevirostris ) behaviour is limited. This study uses data collected by The Cambodian Marine Mammal Conservation Project, to investigate the behavioural responses of Irrawaddy dolphins towards a dead conspecific. During a routine boat survey of Cambodia’s Kep Archipelago, the carcass of an adult female Irrawaddy dolphin was recovered and attached to the stern of the research vessel and promptly towed to the research island for further examination. During this survey, there was a four-fold increase in the number of Irrawaddy dolphin groups observed compared to the seasonal average (post-monsoon), in addition to an atypically positive response towards the research vessel and an atypical increase in the number of behavioural events observed. These behavioural variations were believed to be in response to the towed dead conspecific. The authors propose future dedicated research to assess the complexity of wild Irrawaddy dolphin behaviour, cognition, and awareness, to robustly exemplify the species’ apparent sentience and intelligence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".