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Record W2518136372 · doi:10.1111/mms.12338

Consensus movements by groups of sperm whales

2016· article· en· W2518136372 on OpenAlexafffund
Hal Whitehead

Bibliographic record

VenueMarine Mammal Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie UniversityCetacean Society InternationalNational Geographic Society
KeywordsHeading (navigation)ForagingGeodesySpermGeographyBiologyEcology

Abstract

fetched live from OpenAlex

Abstract When animals live in cohesive groups they need to make consensus decisions about movements. As a very large‐scale example of communal movement, nomadic female sperm whales ( Physeter macrocephalus ) travel about 50 km per day as coherent groups of 10–50 animals spread over several km of ocean. From 543 h of data during which 3,873 headings of small clusters of whales or individual whales were recorded, I quantified the heading behavior of groups foraging off the Galápagos Islands. The groups made both sudden and gradual turns. Using piecewise regression models, I estimate that sudden and gradual turns in heading both occurred at rates of 0.10/h. The mean change in heading was 69° for sudden turns and 84° for gradual turns. The mean duration of gradual turns was 1.3 h, so turns were often slow. Using the recorded headings within 30 min of each of 1,798 focal headings, a regression of heading on time gave a mean rate of turn of the group and error of each focal heading about the mean heading. Absolute heading errors increased with absolute turn rate ( r S = 0.241; P = 0.0000), so turns were often messy. Thus sperm whales often make slow and messy—likely democratic—consensus decisions when groups change heading.

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)0.000
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 score0.160
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.012
GPT teacher head0.225
Teacher spread0.213 · 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

Citations28
Published2016
Admission routes2
Has abstractyes

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