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Record W2967437319 · doi:10.1139/cjz-2019-0064

Alarm calls of the same individual vary during a response to the same predator in Gunnison’s prairie dogs (<i>Cynomys gunnisoni</i>)

2019· article· en· W2967437319 on OpenAlexvenueno aff
W. J. Loughry, Markus Oeser, John L. Hoogland

Bibliographic record

VenueCanadian Journal of Zoology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsnot available
FundersValdosta State UniversityNational Science Foundation
KeywordsPrairie dogPredatorBiologyAlarm signalCynomys ludovicianusALARMEcologySigmodon hispidusZoologyPredation

Abstract

fetched live from OpenAlex

Many animals emit vocalizations in a repetitive series, but are all the calls within a series structurally the same? To answer this question, we recorded the barks of adult female Gunnison’s prairie dogs (Cynomys gunnisoni (Baird, 1855)) during 5 min experimental presentations of several terrestrial stimuli. We measured eight variables (primarily pitch and duration measures) of the first, middle, and last barks in each bout of barking produced by each of 24 females, as well as the duration of inter-bout intervals, the number of barks per bout, and the rate of barking per bout. We found that first barks were significantly longer and higher pitched than middle or last barks. Some of these differences were affected by the number of barks in a bout. Regardless of bark position, barks became longer and lower pitched in later bouts, and inter-bout intervals, number of barks per bout, and the rate of barking per bout all declined in later bouts. Our results show that bark structure can vary even within a single context and within a short period of time. Thus, variation due to call position within and across bouts of calling is a potentially important confound for studies examining other sources of acoustic variation.

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 score0.308
Threshold uncertainty score0.964

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 science0.0010.000
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 head0.015
GPT teacher head0.251
Teacher spread0.235 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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