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Record W2379555687 · doi:10.1093/jmammal/gyw076

Novel Glaucomys volans vocalizations in Indiana and evidence of geographic variation in high frequency communication

2016· article· en· W2379555687 on OpenAlexaboutno aff
Joseph W. Eisinger, John S. Scheibe, Elizabeth A. Flaherty

Bibliographic record

VenueJournal of Mammalogy · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRange (aeronautics)Discriminant function analysisGeographyVariation (astronomy)EcologyTaxonBiologyZoologyStatisticsMathematics

Abstract

fetched live from OpenAlex

Abstract The study of ultrasonic signaling has provided important insights into the ecology of bats and marine mammals, but it is poorly understood in other mammalian taxa. Recently, ultrasonic and high frequency vocalizations were described in southern flying squirrels (Glaucomys volans) in the southeastern United States and Ontario, Canada. Notable differences in many call characteristics suggest regional variation in G. volans communication. We evaluated this observation of regional variation by examining G. volans communication in a little studied portion of their range, the Central Hardwoods Region of the United States. We recorded calls of wild G. volans near West Point, Indiana, with Anabat II ultrasonic recorders. We described frequency and time characteristics of recorded calls, categorized call syllables into types, and used a canonical discriminant function analysis to refine our classification. Our analyses yielded 11 distinct types of G. volans syllables, 7 of which are unlike any calls described in other portions of the species range. This suggests G. volans either communicates in distinct regional dialects or has a much larger call repertoire than previously known. We recommend the creation of locally specific call libraries across the range of G. volans to ensure accuracy in the study of high frequency communication.

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 score0.085
Threshold uncertainty score0.999

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 head0.022
GPT teacher head0.231
Teacher spread0.208 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

Explore more

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