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Variations in the echolocation calls of the European free‐tailed bat

2008· article· en· W1985541772 on OpenAlexaff
Sarah Bayefsky-Anand, Mark D. Skowronski, M. Brock Fenton, Carmi Korine, Marc W. Holderied

Bibliographic record

VenueJournal of Zoology · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsWestern University
FundersJacob Blaustein Center for Scientific Cooperation
KeywordsHuman echolocationBiologySound productionZoologyAcoustics

Abstract

fetched live from OpenAlex

Abstract The echolocation calls of Tadarida teniotis were studied in an outdoor flight enclosure (captive individuals) and in the wild using single microphones or an array of four microphones. Calls were characterized by measures of 10 call variables. Comparison of individual calls recorded on four microphones arrayed in a tetrahedron with 1 m between each microphone revealed that all calls were not equally detectable by all microphones but that there were no significant differences in call features obtained from calls recorded on all four microphones. A comparison of 47 calls recorded by all four microphones showed no significant differences in the features of the four recordings of each call. Analysis of calls of five individuals flying individually in an outdoor flight cage revealed significant individual differences in call features. In the field, T. teniotis used long, narrowband search‐phase calls, usually without harmonics. Analysis of 1876 search‐phase echolocation calls of T. teniotis recorded in the field in Israel and Greece in 2002, 2005 and 2006 showed significant year‐to‐year and site‐to‐site differences in some call features. When flying in the presence of conspecifics, T. teniotis changed their echolocation calls. We found a range of different buzzes in the wild, and based on their structure we attempted to classify them as feeding and social buzzes. The features of individual calls comprising buzzes differed significantly among buzzes, and yet there were no consistent differences between what we classified as feeding and social buzzes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.127

Codex and Gemma teacher scores by category

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

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 head0.026
GPT teacher head0.210
Teacher spread0.185 · 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.

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

Citations16
Published2008
Admission routes1
Has abstractyes

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