MétaCan
Menu
Back to cohort
Record W2026193242 · doi:10.1371/journal.pone.0052987

Spatial and Social Sexual Segregation Patterns in Indo-Pacific Bottlenose Dolphins (Tursiops aduncus)

2013· article· en· W2026193242 on OpenAlexaff
Christine A. Fury, Kathreen E. Ruckstuhl, Peter L. Harrison

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Calgary
FundersNational Parks and Wildlife ServiceSouthern Cross University
KeywordsBottlenose dolphinPredationIndo-PacificBiologyForagingEstuaryZoologyMating systemMatingEcologySeasonal breederSubtropics

Abstract

fetched live from OpenAlex

Sexual segregation seems to be common in bottlenose dolphins, whereby males and females live in different pods that mix mainly for mating. Male dolphins often use aggressive behaviour to mate with females, while females with calves may have different activity and dietary requirements to males and different susceptibility to predation. We investigated the degree of spatial and social sexual segregation in Indo-Pacific bottlenose dolphins (Tursiops aduncus) in a subtropical estuary in Australia. Based on surveys completed over three years, dolphin groups were mostly mixed-sex or female. Mixed-sex groups were found in larger groups in mostly deeper water, whereas, female groups were foraging across all water depths in smaller groups. Aggressive coercive behaviour by males towards females was high, occurring mainly in deeper water, at higher tides, and outside the breeding season. Habitat use by female dolphin groups suggests that shallow tributaries may provide a sanctuary from aggressive males, access to suitable prey items and density for mothers and their calves, or a combination of these factors.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.208
Teacher spread0.178 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations50
Published2013
Admission routes1
Has abstractyes

Explore more

Same venuePLoS ONESame topicMarine animal studies overviewFrench-language works237,207