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Record W3041521193 · doi:10.22621/cfn.v134i1.2331

Into the drink: observation of a novel hunting technique employed by an Eastern Gray Squirrel (<i>Sciurus carolinensis</i>)

2020· article· en· W3041521193 on OpenAlexaffvenue
Alex O. Sutton, Matthew Fuirst, Kristen Bill

Bibliographic record

VenueThe Canadian Field-Naturalist · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSciurus carolinensisSciurusForagingPredationGray (unit)HerbivoreBiologyEcologyFisheryZoologyGeographyHabitat

Abstract

fetched live from OpenAlex

Observations of typically herbivorous species have shown that animals will opportunistically eat animal tissue that is either scavenged or hunted. Squirrels from a number of genera have been observed to hunt prey and consume meat in terrestrial ecosystems. Here we provide evidence of a novel hunting strategy employed by an Eastern Gray Squirrel (Sciurus carolinensis), which has not previously been observed hunting aquatic prey. More rigorous observational studies are needed to determine the extent of this behaviour in Eastern Gray Squirrel populations and whether fishing is a common foraging behaviour for this species.

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)0.000
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: Empirical
Teacher disagreement score0.990
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.026
GPT teacher head0.245
Teacher spread0.218 · 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
Published2020
Admission routes2
Has abstractyes

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