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Record W3154275571 · doi:10.1163/1568539x-bja10086

Conspecific scarring on wild belugas (Delphinapterus leucas) in Cunningham Inlet

2021· article· en· W3154275571 on OpenAlexaffabout
Jackson R. Ham, Malin K. Lilley, Heather M. Hill

Bibliographic record

VenueBehaviour · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsBeluga WhaleBelugaLeucasBottlenose dolphinPopulationAggressionZoologyBiologyGeographyFisheryDemographyEcologyPsychologyArcticDevelopmental psychology

Abstract

fetched live from OpenAlex

Abstract Intra-specific aggression is not frequently observed in wild cetaceans, including belugas. One proxy, identified in past research, that indicates past aggressive behaviour is the presence of rake marks (scars left on skin by the teeth of conspecifics). Behavioural observations of belugas, compared to bottlenose dolphins, suggest that belugas engage in less physically aggressive behaviour; yet, a detailed study of beluga aggressive behaviour remains to be conducted. Beluga intra-specific aggression was assessed by scoring photographs taken from July to August in 2015 at Cunningham Inlet, Canada for the presence/absence and body location of rake marks. Of the 252 belugas analysed, 44% had rake marks. The results suggest that physical aggression occurs comparatively less with only half of the observed beluga population having rake marks compared to almost all bottlenose dolphins previously surveyed. We suggest social structure, skin pigmentation, and/or species-specific behaviours as explanations for the differences in rake marks among species.

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 score0.641
Threshold uncertainty score0.722

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.032
GPT teacher head0.263
Teacher spread0.231 · 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

Citations18
Published2021
Admission routes2
Has abstractyes

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