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Record W4392506227 · doi:10.1061/9780784485330.055

Laboratory Tests Investigating the Influence of Moisture Availability on Frost Heave

2024· article· en· W4392506227 on OpenAlexaff
Caroline Silins, Greg Siemens, W. Andy Take

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsFrost heavingFrost (temperature)MoistureEnvironmental scienceGeotechnical engineeringGeologyMeteorologyGeographyGeomorphology

Abstract

fetched live from OpenAlex

Seasonal frost heave impacts critical infrastructure in northern regions and creates design challenges for engineers. Frost heave issues occur where there are simultaneously a frost susceptible soil, cold temperatures, and a ready supply of water. At more northern latitudes in permafrost zones, conditions exist where the vertical moisture flow could be limited by a frozen layer at depth. However, climate change effects could lead to alternative moisture availability over the life of a project. This paper will explore moisture availability conditions during two laboratory freezing experiments on a frost susceptible soil. Temperature profiles, heave, and water intake data are collected, as well as digital images. Results show frost heave quantity and rate are reduced when moisture access is limited. Instead of increasing in overall volume throughout the test due to both phase change and taking on additional pore water, the dominant outcome is rearrangement of moisture along the vertical profile. Results of the research will serve to inform cold regions engineers on frost heave issues within the context of changing ground conditions due to climate change.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.254
Teacher spread0.224 · 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 designBench or experimental
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

Citations0
Published2024
Admission routes1
Has abstractyes

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