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Record W4404938360 · doi:10.1029/2024wr037537

A Novel Heat Pulse Method in Determining “Effective” Thermal Properties in Frozen Soil

2024· article· en· W4404938360 on OpenAlexaff
Xiaolong Wu, Ying Zhao

Bibliographic record

VenueWater Resources Research · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsGlobal Institute for Water SecurityUniversity of SaskatchewanQueen's University
FundersGovernment of Shandong ProvinceNational Natural Science Foundation of China
KeywordsThermalMaterials sciencePulse (music)Environmental scienceSoil scienceGeotechnical engineeringGeologyThermodynamicsEngineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Abstract Accurate and fast measurements of thermal properties are frequently required for characterizing the heat‐water dynamics in frozen soil. Measuring the thermal properties of frozen soil without inducing ice thaw has proven challenging with conventional heat pulse (HP) methods. In this study, based on an Infinite Line Source (ILS) semi‐analytical model that applies a constant temperature lower than the freezing point at the heat source to prevent the initiation of ice thaw in the frozen soil, we propose a novel HP‐based approach to measure thermal properties, applicable at temperatures below or above 0°C. Laboratory experiments and numerical modeling were utilized to validate the applicability of the approach and optimization strategies of the measurement. We found that the proposed HP‐based approach effectively maintained the maximum spatial temperature below the freezing point and therefore estimated the bulk thermal properties of quartz sand and ice contents. An optimized measurement strategy was proposed to monitor the temperature variations 2–4 cm away from the center of the heat probe after 60 s. This progress can largely facilitate the determination of the thermal properties of multi‐phase and ‐component frozen soil such as thermal conductivity, heat flux, and ice content in cold areas across soil science, hydrology, engineering, and climate science.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.250
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.128
GPT teacher head0.347
Teacher spread0.219 · 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

Citations79
Published2024
Admission routes1
Has abstractyes

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