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Record W3217144598

Rattle calls as individual identifiers in the North American red squirrel (Tamiasciurus hudsonicus).

2021· article· en· W3217144598 on OpenAlexaff
Quinn Eng

Bibliographic record

VenueStudent Research Proceedings · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsMacEwan University
Fundersnot available
KeywordsVocal communicationAnimal communicationCommunicationBiologyEcologyPsychology
DOInot available

Abstract

fetched live from OpenAlex

The rattle calls of North American red squirrels (Tamiasciurus hudsonicus) have been theorized to serve as signals of aggressive territorial intent. These calls are produced by males and females periodically when another is trespassing within the territory or in response to another rattle. However, sometimes individuals rattle in the absence of any obvious stimuli. An alternative explanation for the red squirrel’s rattle call is the need to self-identify, which is often crucial to minimize costly encounters with neighbour and stranger conspecifics, as well as potentially identify intruders and mates. Individual squirrels were trapped and released, at Whitemud Creek, in various locations both within and outside of territory boundaries. Upon release squirrels were monitored for 10 minutes and all rattle vocalizations were recorded. Results indicate that squirrels reliably produced calls, within 10 minutes of release, both within and outside of their territory. These results suggest that the rattle call may function as a form of self identification and not predominantly as an aggressive territorial call. Department: Biology Faculty Mentor: Dr. Shannon Digweed

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.094
GPT teacher head0.416
Teacher spread0.322 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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