A Novel Heat Pulse Method in Determining “Effective” Thermal Properties in Frozen Soil
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".