FEEDING PREFERENCES OF THE MONKEY MIA DOLPHINS: RESULTS FROM A SIMULTANEOUS CHOICE PROTOCOL
Bibliographic record
Abstract
Abstract The semiwild beach‐feeding bottlenose dolphins (Tursiops aduncus) of Monkey Mia, Western Australia, provide an unparalleled opportunity to examine prey preference of this species. In a series of binary‐choice feeding experiments, we took advantage of the animals' willingness to be fed by hand, to explore their preferences for fish species, size, and state (freshly caught or previously frozen). At the end of each beach visit, each dolphin was provided with a pair of fish but allowed to eat only the first one chosen. The dolphins appeared indifferent among the three species of fish offered to them (yellowtail trumpeter, Amniataba caudovittatus; striped trumpeter, Pelates sexlineatus; and western butterfish, Pentapodus vitta), which were of similar body form and matched for mass. Overall, the dolphins showed a slight preference for the larger of two yellowtail trumpeter offered, suggesting the capability for rational choice when there was a basis for it (most likely energy in this case), although there was considerable individual variation. The dolphins did not distinguish between freshly caught and previously frozen yellowtail. The methodology we describe can be used to generate data of potential value for understanding food and habitat selection of wild dolphins, and for modifying management practices for semiwild dolphins at Monkey Mia and elsewhere.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".