Magnetic Resonance Imaging as a tool for investigating frost heave dynamics: a new experimental setup and application
Bibliographic record
Abstract
Frost heave in frozen soils is a critical geotechnical phenomenon driven by thermal gradients, moisture migration, and ice formation. Understanding this process is essential for ensuring infrastructure stability in cold regions. While previous studies have contributed to understanding frost heave mechanisms, they provide limited insight into local changes within the sample during freezing, particularly regarding the distribution of unfrozen water content. To address this gap, this study introduces the development of a new experimental setup, specifically designed for Magnetic Resonance Imaging (MRI) to investigate the frost heave behavior of sandy soils. MRI was used to track the distribution of the local amount of unfrozen water content during freezing. Frost heave tests were conducted on saturated sandy soils. The testing program and the experimental results are presented and discussed, focusing on the freezing point, temperature evolution at different elevations within the specimens, water uptake monitoring, local water content distribution, and frost heave progression. This new apparatus offers a realistic laboratory approach to studying the effect of freezing and thawing on soils.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".