Chemical factors in soil freezing and frost heave
Bibliographic record
Abstract
Chemical factors that are essential in frost heaving of soils are examined through consideration of the process of ice formation in soils and the role of temperature gradients in generating water potential gradients in freezing soils. Unfrozen films are maintained around soil particles in frozen soils. The osmotic potentials at the ice–water interface of the unfrozen films and in the frozen fringe, the thin zone between the frozen and unfrozen soil, generated by dissolved salts and exchangeable cations that satisfy soil particle surface charge, are controlled by the local temperature. The coldest location and the most negative osmotic potentials at the ice–water interface are located immediately below the base of the ice lens, in the unfrozen films that separate the underlying soil particles from the ice lens. An osmotic potential gradient is generated because the osmotic potential at the water–ice interface in the frozen fringe becomes less negative with increasing temperature and distance from the ice lens. As water freezes onto the ice lens, re-supply of water to the unfrozen film along the osmotic potential gradient is the temperature-gradient-induced mechanism that generates the force that lifts the overlying frozen soil. Models that recognize this driving mechanism should improve predictions of soil freezing and frost heave, analysis of contaminant transport in freezing and frozen soils, and other aspects of the soil-freezing and frost-heave processes.
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 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".