Change in Ice Lens Formation for Saline and Non-Saline Devon Silt as a Function of Temperature and Pressure
Bibliographic record
Abstract
During the freezing of a fine grained soil, ice lens formation changes the structure of the soil and results in frost heave. The formation of the ice lenses is very complex and dynamic. The results are presented from laboratory freezing tests on saturated Devon silt, a frost-susceptible soil. A variety of pore-water salinities, vertical pressures, and temperature gradients were used to investigate the different effects on the freezing process and the formation of the ice lenses. Using a novel experimental methodology, ice lens growth at the pore scale was observed. Fluorescein was dissolved in the pore water, which allowed to locate unfrozen water under UV light. In this manner it was possible to visually observe and measure the ice lens growth ahead of and behind the frozen fringe. It is visually shown that the thickness of the ice lenses, the distances between the ice lenses and the thickness of the frozen fringe change with changing temperature gradient, vertical pressure and salinity. In addition, the ice structure within the saline soils became more three dimensional and irregular compared to the non-saline samples, where the ice lenses develop over the entire cross section of the sample.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".