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Record W4396773080 · doi:10.1061/9780784485460.023

Case Study of the Thermal Regime of Permafrost underneath the Airstrips near Hudson Bay Coast, Canada

2024· article· en· W4396773080 on OpenAlexaffabout
Xiangbing Kong, Guy Doré

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalUniversité du Québec à Rimouski
Fundersnot available
KeywordsPermafrostBayGeologyOceanographyPhysical geographyGeography

Abstract

fetched live from OpenAlex

Transportation infrastructure is of vital importance to the local communities in Nunavik, Quebec, Canada. Permafrost degradation results in infrastructure failures and affects social and economic development. The case study of airstrip embankment at Salluit was selected to help improve the understanding of ground thermal conditions in Nunavik. This paper presents the analysis of thermal data collected at the Salluit airstrip and one two-dimensional geothermal model developed to reproduce the subsurface thermal regime under the centerline of airstrip embankment. The measured embankment dimension and air temperature were used as the inputs to improve the model accuracy. Field measurement of ground temperatures for a two-year monitoring period from 2016 to 2018 was used to calibrate the model developed. This study provided valuable information to investigate the vulnerability of the airstrips using the simulation approach in response to climate warming effects in Nunavik.

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)0.000
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 score0.019
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.039
GPT teacher head0.232
Teacher spread0.193 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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