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Record W2025545481 · doi:10.1139/z00-117

Seasonal home range size and philopatry in two northern white-tailed deer populations

2000· article· en· W2025545481 on OpenAlexfundvenueaboutno aff
Louis Lesage, Michel Crête, Jean Huot, André Dumont, Jean‐Pierre Ouellet

Bibliographic record

VenueCanadian Journal of Zoology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOdocoileusPhilopatryBiologyHome rangePopulation densityIntraspecific competitionPopulationRange (aeronautics)EcologyForageCompetition (biology)Population sizeZoologyHabitatBiological dispersalDemography

Abstract

fetched live from OpenAlex

From 1994 to 1997, we compared summer and winter space utilisation by two white-tailed deer (Odocoileus virginianus) populations wintering in adjacent areas in southeastern Quebec characterised by deep snow cover. One population lived at low density (10 deer/km 2 ) with access to abundant forage in winter (127 000 twigs/ha), whereas for the other, high-density population (20 deer/km 2 ), forage availability was reduced (68 000 twigs/ha). Because of intraspecific competition for resources, we predicted that deer in the high-density population would have smaller home ranges, would exhibit greater philopatry, and would be more likely to disperse. Deer from both populations occupied summer home ranges that were similar in size (1182 ha for adult males; 1102 ha for adult females; 6033 ha for yearling males; 2528 ha for yearling females) but much larger than home ranges observed elsewhere in North America. The high-density population showed a higher level of philopatry than the low-density population during winter but not during summer. Most deer remained migratory during the study (n = 93) but 4 of the 5 that dispersed were from the high-density population. We speculate on the ability of white-tailed deer populations facing severe winters to adapt to using large home ranges in summer. Our results shed light on how wintering areas appear and expand.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.991

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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

Citations128
Published2000
Admission routes3
Has abstractyes

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