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Record W2030031192 · doi:10.1139/x02-002

Interrelationships among northern flying squirrels, truffles, and microhabitat structure in Sierra Nevada old-growth habitat

2002· article· en· W2030031192 on OpenAlexvenueno aff
Sanjay Pyare, William S. Longland

Bibliographic record

VenueCanadian Journal of Forest Research · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSnagUnderstoryHabitatAbundance (ecology)EcologyCoarse woody debrisPredationBiologyForest floorGeographyEcosystem

Abstract

fetched live from OpenAlex

During 1997-1998, we investigated the influence of both the relative abundance of truffles, preferred food items, and microhabitat structure on the occurrence of northern flying squirrels (Glaucomys sabrinus Shaw) in old-growth forest habitat of the Sierra Nevada Range, U.S.A. Following live-trapping sessions, we searched the forest floor for truffle diggings and sampled the soil for truffles. Diggings were more abundant where flying squirrels were captured, suggesting squirrels were active near areas of the forest floor where truffles had recently been excavated. The frequency of sampling plots with truffles was higher where squirrels were captured, further suggesting preferences for microhabitats where truffles were more abundant. We also measured 15 microhabitat variables at trap stations to evaluate the influence of aboveground microhabitat characteristics on squirrel occurrence. Results indicated that flying squirrels preferred microhabitats with understory cover, which may minimize predation from aerial predators like spotted owls (Strix occidentalis Merriam). Neither abundance of coarse woody debris, a feature conducive to fungal growth, nor the abundance of potential nesting sites (i.e., snags) measurably influenced squirrel occurrence. While various aboveground forest-microhabitat characteristics affect the use of old-growth forests by flying squirrels, these animals refine their use of these forests based on fine-scale changes in the availability of a highly preferred and ephemeral food item.

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)0.001
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 score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.276
Teacher spread0.228 · 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

Citations53
Published2002
Admission routes1
Has abstractyes

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