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Record W4392372867 · doi:10.22230/jem.2024v24n1a631

Guidelines for Winter Recreation near Wolverine Dens in Montane Western North America

2024· article· en· W4392372867 on OpenAlexaff
Doris Hausleitner, Andrea Kortello, Mirjam Barrueto, William L. Harrower, John A. Krebs

Bibliographic record

VenueJournal of Ecosystems and Management · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of CalgaryMinistry of ForestsSelkirk College
Fundersnot available
KeywordsRecreationMontane ecologyDisturbance (geology)GeographyRecreational useOccupancyForestryPhysical geographyEcologyEnvironmental resource managementEnvironmental science

Abstract

fetched live from OpenAlex

Wolverine den in snowy areas with boulders or woody debris at or below tree line in montane western North America. They have naturally low reproductive rates, a fidelity to denning areas, and a sensitivity to human presence during denning. The goal was to synthesize existing ecological information for denning wolverine and identify risks from human presence in the categories of timing, distance, footprint, pattern of use, and frequency of use. The authors suggest commercial tenure holders and private users keep recreation in the low-risk category to minimize disturbance on denning females. Denning area surveys should be conducted prior to tenure application or renewals and dens can be identified by a concentration of tracks over more than three weeks from January 15 to May 15. Recreation should be restricted within a 5-km radius of confirmed dens during this window. Best practices include limiting the number of groups and concentrating movement on existing linear features as wolverine are sensitive to disturbance at a very low intensity of use and are at greatest risk when disturbances are dispersed and unpredictable.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.004

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 head0.025
GPT teacher head0.271
Teacher spread0.246 · 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 designNot applicable
Domainnot available
GenreMethods

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 routes1
Has abstractyes

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