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Record W2020453261 · doi:10.1139/z09-137

Influence of patch- and landscape-level attributes on the movement behavior of raccoons in agriculturally fragmented landscapes

2010· article· en· W2020453261 on OpenAlexvenueno aff
James C. Beasley, Olin E. Rhodes

Bibliographic record

VenueCanadian Journal of Zoology · 2010
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHome rangeHabitatRange (aeronautics)EcologyBiologyEcosystem

Abstract

fetched live from OpenAlex

We tested the hypotheses that movement rates and home range sizes of raccoons ( Procyon lotor (L., 1758)) inhabiting a highly fragmented landscape would vary in response to local and landscape-level habitat characteristics. Raccoons occupying small forest patches containing limited water sources maintained larger home ranges than raccoons with home ranges established in large forest patches containing abundant sources of water. Raccoons occupying large (>25 ha) forest patches exhibited minimal interindividual variance in home range size compared with raccoons monitored in patches <25 ha. This differing pattern of variance in home range size suggests that critical resources were more widely dispersed within and among small patches, forcing raccoons occupying smaller patches to utilize larger and more spatially disparate areas to satisfy their metabolic and reproductive needs. Movement rates of raccoons were positively related to home range size, although movement rates of males (246.9 m/h) exceeded those of females (188.3 m/h). Moreover, movements of male raccoons primarily were concentrated along forest–agriculture interfaces, whereas female movements were concentrated in forest interiors. Our results indicate that raccoons have modified their movement behavior in agricultural ecosystems in response to the discontinuous nature of resources and suggest that the extent of raccoon space use in these ecosystems is strongly influenced by the availability of non-agricultural resources.

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 score0.081
Threshold uncertainty score0.930

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)0.0000.000

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 head0.032
GPT teacher head0.266
Teacher spread0.234 · 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

Citations57
Published2010
Admission routes1
Has abstractyes

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