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Record W4405900168 · doi:10.1038/s41598-024-84000-3

White-tailed deer limit their spatio-temporal overlap with hikers in a protected area

2024· article· en· W4405900168 on OpenAlexafffundabout
Virginie Millien, Frédérique Truchon, Martin‐Hugues St‐Laurent

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversité du Québec à RimouskiMcGill UniversityMcGill University Health Centre
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOdocoileusRecreationHabitatWildlifeEcosystemGeographyEcologyNature reserveWildlife managementBiology

Abstract

fetched live from OpenAlex

The pressure on ecosystems resulting from outdoor recreational activities is increasing globally. Protected areas offer to large mammals refugia free of hunting with greater access to food resources, but the presence of humans for recreation in these areas may induce changes in behaviour, activity pattern, and habitat use. We used camera traps to model the spatial distribution and temporal activity of the white-tailed deer (Odocoileus virginianus) in a nature reserve located close to Montreal, the second largest metropole in Canada. We showed that deer limit their overlap with the public both across space and over time. White-tailed deer exhibited their highest activity level early in the morning, prior to the reserve's opening hours, indicating temporal avoidance of the public. Deer were also detected at a higher rate when further away from the hiking trail, although in less suitable habitat, indicating spatial avoidance. Human recreational activities in nature reserves open to the public may thus have an indirect effect on ecosystems, by altering the habitat use and behaviour of wildlife.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.107
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.011
GPT teacher head0.198
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2024
Admission routes3
Has abstractyes

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