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Record W2012942997 · doi:10.1139/z06-009

Seasonal changes in home ranges of Abert's squirrels: impact of mating season

2006· article· en· W2012942997 on OpenAlexvenueno aff
Andrew J. Edelman, John L. Koprowski

Bibliographic record

VenueCanadian Journal of Zoology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
FundersU.S. Forest ServiceT and EAnimal Behavior Society
KeywordsHome rangeBiologyMatingSeasonal breederEcologyRange (aeronautics)HabitatMating system

Abstract

fetched live from OpenAlex

We compared home ranges of introduced Abert's squirrels (Sciurus aberti Woodhouse, 1853) in mixed-conifer forests of Arizona during non-mating and mating seasons. Because Abert's squirrels are reported to depend on ponderosa pine (Pinus ponderosa P. & C. Lawson) forests, the mixed-conifer forest in our study represented a novel habitat. Home-range size, home-range overlap with females, and movement distances increased for males from non-mating to mating seasons. Home-range size and overlap characteristics of females remained consistent between seasons, but movement distances were reduced during the mating season. Males probably increased home-range size, home-range overlap with females, and movement distances during the mating season to maximize contact with scarce females. Home-range size and overlap characteristics of female Abert's squirrels likely remained stable between seasons because females do not search for mates. Restricted movements by females during the mating season may be due to changes in resource use in preparation for reproduction. Non-mating season home ranges in our study were smaller than home ranges observed in ponderosa pine forest. Abert's squirrels in mixed-conifer forest may have small home ranges because resource quality is higher than in ponderosa pine forest or competition for space with co-occurring Mount Graham red squirrels (Tamiasciurus hudsonicus grahamensis (J.A. Allen, 1894)).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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.

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

Citations41
Published2006
Admission routes1
Has abstractyes

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