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Record W2762312029 · doi:10.1163/1568539x-00003451

The baroque potheads: modification and embellishment in repeated call sequences of long-finned pilot whales

2017· article· en· W2762312029 on OpenAlexafffund
Elizabeth Zwamborn, Hal Whitehead

Bibliographic record

VenueBehaviour · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaEli Lilly and Company
KeywordsCommunicationMorphingTransition (genetics)Variation (astronomy)Duration (music)Call durationComputer scienceAcousticsSpeech recognitionPsychologyBiologyTelecommunicationsArtificial intelligencePhysicsAstrophysics

Abstract

fetched live from OpenAlex

Vocal variation within calls that are generally stereotyped suggests multiple simultaneous functions. These vocal cues may be especially important for group-living species. We describe two fundamental call transition types within repeated call sequences of long-finned pilot whales (Globicephala melas): embellishment — discrete changes to a specific part of a call — and morphing — non-discrete small changes across a call. Of transitions between consecutive calls, 31% were embellished and 20% morphed. Modifications between pairs of consecutive calls were often followed by another modification of the same type, with sequences of embellished transitions generally alternating between ornamentation and simplification. Ten classes of embellishment varied in rate of occurrence as well as temporal location within a call. Most common were the addition/deletion of pulsed or tonal elements. Functions of these modifications could include conveying information on location or the emotional state of the signaller, or they could be products of vocal innovation.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.047
GPT teacher head0.295
Teacher spread0.248 · 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

Citations6
Published2017
Admission routes2
Has abstractyes

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