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Record W4391512331 · doi:10.5962/p.363404

Diets of Northern Flying Squirrels, Glaucomys sabrinus, in southeast Alaska

2002· article· en· W4391512331 on OpenAlexvenueno aff
Sanjay Pyare, Winston P. Smith, Jeffrey V. Nicholls, Joseph A. Cook

Bibliographic record

VenueThe Canadian Field-Naturalist · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyZoologyBiology

Abstract

fetched live from OpenAlex

We examined the diet of the Northern Flying Squirrel (Glaucomys sabrinus) during summer and autumn seasons in temperate rain-forest habitat of Southeast Alaska, a region in which the ecology of this species is poorly understood.Truffles, a food item that is commonly consumed by squirrels during snow-free periods outside of Alaska, were present less frequently in squirrel feces than two other food items, epigeous fungi and vegetation, although no food item dominated fecal composition.Truffles were less frequent in fecal samples from mixed-conifer muskeg habitats than from old-growth forest habitats.Overall, we found that squirrels consumed a total of five truffle genera; Elaphomyces and Hymenogaster being the most common.Compared to populations in the western contiguous United States, squirrel populations in Southeast Alaska consumed truffles less frequently and consumed a smaller total number of truffle genera.In addition, samples from individual squirrels in Alaska tended to contain fewer genera than samples from the contiguous United States.Finally, squirrels in Alaska consumed other food items such as vascular vegetation, lichens, and mushrooms more frequently than squirrels in other geographic areas.These patterns suggest that the association between flying squirrels and truffles may be relatively weaker in Southeast Alaska than has been documented elsewhere.Consequently, additional information on life history and ecology of flying squirrels is warranted before forest management guidelines can be developed.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.011
GPT teacher head0.195
Teacher spread0.184 · 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; both teacher heads agree on what is shown here.

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

Citations13
Published2002
Admission routes1
Has abstractyes

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