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Record W2288601032 · doi:10.1093/jmammal/gyw010

Nest attendance of lactating red squirrels (<i>Tamiasciurus hudsonicus</i>): influences of biological and environmental correlates

2016· article· en· W2288601032 on OpenAlexaff
Emily K. Studd, Stan Boutin, Andrew G. McAdam, Murray M. Humphries

Bibliographic record

VenueJournal of Mammalogy · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsNest (protein structural motif)LitterEcologyOffspringAttendanceBiologyThermoregulationAnimal scienceZoology

Abstract

fetched live from OpenAlex

Time allocation by lactating mammals is a reconciliation of often opposing nutritional and thermal demands of both the offspring and mother. Here we test the hypothesis that nest attendance patterns of lactating red squirrels ( Tamiasciurus hudsonicus ) vary with environmental and biological traits that relate to the thermoregulation of mothers and their offspring. We used temperature dataloggers to continuously record nest attendance and activity of free-ranging females with neonatal ( n = 45) and preemergent ( n = 53) litters. Lactating red squirrels concentrated activity out of the nest around the warmest parts of winter days and the coldest parts of summer days. Both younger and lighter litters had mothers that spent more time in the nest and shorter periods of time out of the nest. Females matched timing of activity within a day to the hourly air temperatures closest to their thermal neutral zone, serving to reduce individual and offspring thermoregulatory costs associated with activity. Nest attendance patterns appear to be constrained by the thermal, and possibly nutritional, requirements of the litter with females fine-tuning behavior to match constantly changing environmental and biological conditions, consistent with reduced energetic costs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.756

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 studies0.0000.001
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 head0.015
GPT teacher head0.236
Teacher spread0.220 · 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

Citations21
Published2016
Admission routes1
Has abstractyes

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