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Record W2521328762 · doi:10.1002/jwmg.21137

Distribution of female wolverines relative to snow cover, Alberta, Canada

2016· article· en· W2521328762 on OpenAlexafffundabout
Shevenell M. Webb, Robert B. Anderson, Douglas L. Manzer, Bill Abercrombie, Brian Bildson, Matthew A. Scrafford, Mark S. Boyce

Bibliographic record

VenueJournal of Wildlife Management · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of AlbertaAlberta Conservation Association
FundersU.S. Fish and Wildlife ServiceAlberta Conservation Association
KeywordsSnow coverGeographyCover (algebra)Distribution (mathematics)Physical geographySnowMeteorologyEngineeringMathematics

Abstract

fetched live from OpenAlex

ABSTRACT Wolverines ( Gulo gulo ) in the contiguous United States have been considered for protection under the Endangered Species Act, most recently based on the value of deep snow for the duration of the wolverine's denning season. We examined evidence for an obligate relationship between wolverines and spring snow cover using camera traps and long‐term fur harvests in Alberta. The proportion of traplines that harvested ≥1 wolverine was highest in the northwest Boreal Forest (0.3), where mean wolverine harvest density increased by 75% from the 1990s to 2000s. There was no difference in percent spring snow cover on traplines with a female ( n = 81) or no female ( n = 416) wolverine harvest in the Boreal Forest. Further, all female wolverines ( n = 8) positively identified from camera traps in the Boreal Forest, including 5 lactating females, were located within townships predicted to have no spring snow cover. Long‐term harvests and evidence of reproduction in areas with low amounts of spring snow cover in the Boreal Forest of northern Alberta suggest that wolverines may be more flexible in their distribution than previously assumed. © 2016 The Wildlife Society.

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 score0.153
Threshold uncertainty score0.983

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)0.0010.000

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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

Citations25
Published2016
Admission routes3
Has abstractyes

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