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Record W2607181271 · doi:10.1017/9781107280519

Animal Vocal Communication

2017· book· en· W2607181271 on OpenAlexaff
Eugene S. Morton

Bibliographic record

VenueCambridge University Press eBooks · 2017
Typebook
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsYork University
Fundersnot available
KeywordsVocal communicationMimicryAnimal communicationCommunicationLaughterEmphasis (telecommunications)Cognitive scienceAnimal behaviorIdeal (ethics)PsychologyBiologyComputer scienceNeuroscienceEcologyEpistemologyZoology

Abstract

fetched live from OpenAlex

How do animals communicate using sounds? How did animal vocal communication arise and evolve? Exploring a new way to conceptualize animal communication, this new edition moves beyond an earlier emphasis on the role of senders in managing receiver behaviour, to examine how receivers' responses influence signalling. It demonstrates the importance of the perceiver role in driving the evolution of communication, for instance in mimicry, and thus shifts the emphasis from a linguistic to a form/function approach to communication. Covering a wide range of animals from frogs to humans, this new edition includes new sections on human prosodic elements in speech, the vocal origins of smiles and laughter and deliberately irritating sounds and is ideal for researchers and students of animal behaviour and in fields such as sensory biology, neuroscience and evolutionary biology.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.150
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.253
Teacher spread0.221 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations22
Published2017
Admission routes1
Has abstractyes

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