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Record W2946854071 · doi:10.1680/jenge.17.00036

Seasonal deformations under a road embankment on degrading permafrost in Northern Canada

2018· article· en· W2946854071 on OpenAlexafffundabout
David Kurz, David Flynn, Marolo Alfaro, Lukas U. Arenson, Jim Graham

Bibliographic record

VenueEnvironmental Geotechnics · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsBGC Engineering (Canada)University of ManitobaKGS Group (Canada)
FundersUniversity of Manitoba
KeywordsPermafrostLeveeGroundwaterGeotechnical engineeringGeologyFrost (temperature)Environmental scienceFrost heavingSettlement (finance)Human settlementHydrology (agriculture)GeomorphologyEngineering

Abstract

fetched live from OpenAlex

Permafrost degradation is a major concern in cold regions that are warming because of climate change. To assist in understanding the process, ground temperatures, lateral and vertical deformations and groundwater pressures were measured for 6 years under a road embankment in northern Manitoba, Canada. The road surface requires ongoing maintenance due to irregular settlements. The field data allowed the calibration of numerical modelling that related deformations to ground temperatures. It confirmed the presence of remnant permafrost under the centre of the embankment, but none under the midslope or toe. This ‘frost bulb’ plays an important role in the ground thermal regime, the groundwater pressures, the observed deformations and, ultimately, necessary road maintenance.

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 score0.032
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.016
GPT teacher head0.199
Teacher spread0.184 · 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

Citations7
Published2018
Admission routes3
Has abstractyes

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