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Record W4310102615 · doi:10.1139/cjz-2022-0121

Interindividual communication by bats via echolocation

2022· article· en· W4310102615 on OpenAlexafffundvenue
Robert M. R. Barclay, David S. Jacobs

Bibliographic record

VenueCanadian Journal of Zoology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNational Research FoundationUniversity of Cape TownUniversity of Calgary
KeywordsHuman echolocationBiologyEavesdroppingPopulationEcologyInformation transferAnimal communicationZoologyCommunicationPsychologyDemographyComputer science

Abstract

fetched live from OpenAlex

The majority of over 1400 species of bats produce echolocation calls of diverse designs as a means of obtaining information about their surroundings, including the presence of prey. These calls also have the potential to contain information about the caller that can be used by other bats. We review the evidence for information transfer from echolocating bats to intended or unintended conspecifics and heterospecifics. Analysis of call structure and playback experiments on over 50 species in 11 families demonstrate that information regarding the species, population, sex, age, size, and individual identity of the caller is often contained within the calls, and in some cases can be recognized and used by other individuals. Intentional or unintentional (eavesdropping) communication occurs in feeding and roosting situations, as well as between individuals in social interactions such as in mate choice and between mothers and young. We also assess limitations of the research to date and suggest avenues for future research.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.017
GPT teacher head0.203
Teacher spread0.186 · 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

Citations10
Published2022
Admission routes3
Has abstractyes

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