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FEEDING PREFERENCES OF THE MONKEY MIA DOLPHINS: RESULTS FROM A SIMULTANEOUS CHOICE PROTOCOL

2003· article· en· W2134138032 on OpenAlexaff
Lawrence M. Dill, Elizabeth S. Dill, D. Canham Charles

Bibliographic record

VenueMarine Mammal Science · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFisheryPredationBiologyFish <Actinopterygii>PreferenceCetaceaHabitatGeographyZoologyEcologyStatistics

Abstract

fetched live from OpenAlex

A bstract 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.023
GPT teacher head0.261
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2003
Admission routes1
Has abstractyes

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