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Record W3127115056 · doi:10.1139/as-2019-0007

Low vulnerability of Arctic fox dens to climate change-related geohazards on Bylot Island, Nunavut, Canada

2021· article· en· W3127115056 on OpenAlexafffundvenueabout
Florence Lapierre Poulin, Daniel Fortier, Dominique Berteaux

Bibliographic record

VenueArctic Science · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsCenter for Northern StudiesUniversité du Québec à Rimouski
FundersFonds de recherche du Québec – Nature et technologiesNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsArctic foxClimate changeVulpesLagopusArcticGeographyPermafrostVulnerability (computing)EcologyWildlifePhysical geographyPredationBiology

Abstract

fetched live from OpenAlex

Climate change increases the risk of severe alterations to essential wildlife habitats. The Arctic fox (Vulpes lagopus (Linnaeus, 1758)) uses dens as shelters against cold temperatures and predators. These dens, needed for successful reproduction, are generally dug into the active layer on top of permafrost and reused across multiple generations. We assessed the vulnerability of Arctic fox dens to the increasing frequency of geohazards (thaw settlement, mass movements, and thermal erosion) that is arising from climate change. On Bylot Island (Nunavut, Canada) we developed, and calculated from field observations, a qualitative vulnerability index to geohazards for Arctic fox dens. Of the 106 dens studied, 14% were classified as highly vulnerable, whereas 17% and 69% had a moderate and low vulnerability, respectively. Vulnerability was not related to the probability of use for reproduction. Although climate change will likely impact Arctic fox reproductive dens, such impact is not a major threat to foxes of Bylot Island. Our research provides the first insights into the climate-related geohazards potentially affecting Arctic fox ecology in the next decades. The developed method is flexible and could be applied to other locations or other species that complete their life cycle in permafrost regions.

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 score0.023
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.030
GPT teacher head0.252
Teacher spread0.222 · 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
Published2021
Admission routes4
Has abstractyes

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Same venueArctic ScienceSame topicClimate change and permafrostFrench-language works237,207