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Record W4319074691 · doi:10.1038/s43247-023-00685-w

Projected decrease in trail access in the Arctic

2023· article· en· W4319074691 on OpenAlexafffundabout
James D. Ford, Dylan G. Clark, Luke Copland, Tristan Pearce, Sherilee L. Harper, Lea Berrang‐Ford, César Cárcamo, Patricia García, Shuaib Lwasa, Didacus B. Namanya, Mark New, Carol Zavaleta-Cortijo

Bibliographic record

VenueCommunications Earth & Environment · 2023
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of AlbertaUniversity of Northern British ColumbiaUniversity of OttawaCanadian Institute for Advanced Research
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of LeedsArcticNetUniversity of Ottawa
KeywordsLivelihoodArcticClimate changeGeographyEnvironmental resource managementSea iceThe arcticGlobal warmingAdaptation (eye)Environmental sciencePhysical geographyOceanographyAgricultureMeteorology

Abstract

fetched live from OpenAlex

Transportation systems in northern Canada are highly sensitive to climate change. We project how access to semi-permanent trails on land, water, and sea ice might change this century in Inuit Nunangat (the Inuit homeland in northern Canada), using CMIP6 projections coupled with trail access models developed with community members. Overall trail access is projected to diminish, with large declines in access for sea ice trails which play a central role for Inuit livelihoods and culture; limits to adaptation in southern regions of Inuit Nunangat within the next 40 years; a lengthening of the period when no trails are accessible; and an unequal distribution of impacts according to the knowledge, skills, equipment, and risk tolerance of trail users. There are opportunities for adaptation through efforts to develop skillsets and confidence in travelling in more marginal environmental conditions, which can considerably extend the envelope of days when trails are accessible and months when this is possible. Such actions could reduce impacts across emissions scenarios but their potential effectiveness declines at higher levels of global warming, and in southern regions only delays when sea ice trails become unusable.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.140
GPT teacher head0.411
Teacher spread0.271 · 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.

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

Citations18
Published2023
Admission routes3
Has abstractyes

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