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Record W4414261091 · doi:10.1139/as-2025-0004

Permafrost characterization, mapping, modelling, and knowledge sharing in support of construction of community roads and trails over sensitive tundra. Kugluktuk, Nunavut, Canada

2025· article· en· W4414261091 on OpenAlexafffundvenueabout
Allard Michel, Stéphanie Coulombe, Marc-André Ducharme, Samuel Bilodeau, Larry Adjun, Leesee Papatsie, Samuel Gagnon

Bibliographic record

VenueArctic Science · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalGovernment of NunavutCIMA+ (Canada)Parks CanadaMinistère des Ressources naturelles et des ForêtsCenter for Northern Studies
FundersWeston Family FoundationGovernment of Nunavut
KeywordsPermafrostTundraTerrainKnowledge sharingArcticClimate changeWetlandHabitat

Abstract

fetched live from OpenAlex

As part of an Inuit-led initiative, a collaborative research project combined scientific methods and Inuit knowledge to design an environmentally friendly and climate change-resilient access route from the community of Kugluktuk to Kugluk Territorial Park, in Western Nunavut, Canada. The project aimed to improve community access to the park and surrounding lands while protecting the tundra ecosystem and ensuring the infrastructures could withstand the impacts of climate change. To achieve these goals, two types of light infrastructure were constructed: (1) a summer road on a thin (≤50 cm) embankment extending to the park entrance, and (2) a locally designed wooden boardwalk trail laid on the tundra within the park. One key challenge for this infrastructure project involved crossing ∼379 ice wedges along the route. Among the applied methods, thermal modelling revealed that filling ice wedge troughs with soil before construction will improve road stability. The numerical simulations also indicated that the “floating” boardwalk trail maintained natural ground temperatures and prevented tundra degradation by ATV (all terrain vehicles) trampling. Training youth and exchanging knowledge between scientists and community members were important components of the project.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

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

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 head0.032
GPT teacher head0.246
Teacher spread0.214 · 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

Citations1
Published2025
Admission routes4
Has abstractyes

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