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Record W4249183712 · doi:10.1139/z01-090

Test of the prey-base hypothesis to explain use of red squirrel midden sites by American martens

2001· article· en· W4249183712 on OpenAlexvenueno aff
Dean E. Pearson, Leonard F. Ruggiero

Bibliographic record

VenueCanadian Journal of Zoology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMiddenPinus contortaBiologyEcologySpecies richnessAbundance (ecology)Predation

Abstract

fetched live from OpenAlex

We tested the prey-base hypothesis to determine whether selection of red squirrel (Tamiasciurus hudsonicus) midden sites (cone caches) by American martens (Martes americana) for resting and denning could be attributed to greater abundance of small-mammal prey. Five years of livetrapping at 180 sampling stations in 2 drainages showed that small mammals, particularly red-backed voles (Clethrionomys gapperi) and shrews (Sorex spp.), were more abundant at midden sites than at non-midden sites. However, logistic regression indicated that middens occurred in spruce–fir (Picea engelmannii – Abies lasiocarpa) stands, being correlated with decreasing lodgepole pine (Pinus contorta) basal area, decreasing distance to water, and increasing canopy cover. Since middens were not randomly distributed, we constructed multiple linear regression models to determine the variability in small-mammal abundance and species richness attributable to structural and landscape variables. Regression models indicated that abundance of small mammals, red-backed voles, and uncommon small mammals could be predicted from structural and landscape variables, but midden presence did not significantly improve these models. Midden presence was a significant but weak predictor of small-mammal species richness. Our data do not support the prey-base hypothesis for explaining martens' selection of resting and denning sites near red squirrel middens at the scales we tested.

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)0.001
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 score0.173
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.029
GPT teacher head0.218
Teacher spread0.189 · 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

Citations11
Published2001
Admission routes1
Has abstractyes

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