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Record W2165131760 · doi:10.1139/z03-143

Food-hoarding behavior of gray squirrels and North American red squirrels in the central hardwoods region: implications for forest regeneration

2003· article· en· W2165131760 on OpenAlexvenueno aff
Jacob R. Goheen, Robert K. Swihart

Bibliographic record

VenueCanadian Journal of Zoology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
FundersAmerican Society of Mammalogists
KeywordsSciurus carolinensisSciurusBiologyEcologyDeciduousPredationHoarding (animal behavior)Home rangeGray (unit)Interspecific competitionForagingHabitat

Abstract

fetched live from OpenAlex

The North American red squirrel (Tamiasciurus hudsonicus) has expanded its geographic range into the state of Indiana concurrently with a decline in populations of gray squirrels (Sciurus carolinensis) throughout portions of the central hardwoods region of the United States that have been converted to intensive agriculture. Red squirrels construct larder hoards and function as seed predators throughout much of their geographic range. In contrast, gray squirrels construct scatter hoards and thus function as seed dispersers in addition to eating seeds. We conducted field observations to discern whether hoarding behavior differed between the two species in a deciduous forest stand near the southern limit of the range of red squirrels. Red squirrels were more likely to hoard walnuts and acorns in larders or trees, whereas gray squirrels were more likely to scatter-hoard mast items. We present a simple model to illustrate the potential impact of interspecific differences in hoarding on germination success of black walnut. Our results suggest that red squirrels are unable to compensate completely for the loss of gray squirrels as seed dispersers in portions of the central hardwoods region that have been transformed by agriculture.

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 score0.373
Threshold uncertainty score0.637

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)0.0000.000

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 head0.033
GPT teacher head0.252
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

Citations43
Published2003
Admission routes1
Has abstractyes

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