Responses of ice–soil mixtures to ice melting
Bibliographic record
Abstract
Numerous ice–soil-mixture landslide dams have formed in the cryosphere such as the Tibetan Plateau and resulted in disastrous consequences after these dams broke. The dam forming materials are often ice–soil mixtures that consist of graded soil particles and fragmented ice particles. The performance of such mixtures when subject to ice melting has rarely been studied; yet understanding the thermal-hydro-mechanical behavior of such ice–soil mixtures is essential for mitigating glacier hazards. In this study, the mechanical responses of ice–soil mixture to ice melting were investigated using an advanced stress- and temperature-controlled triaxial apparatus. Ice–soil mixtures with various initial ice contents were tested under different stress states. In each test, the progression of ice melting, local and global deformation, and post-melting stress–strain behavior were measured and evaluated. The test program led to the first batch of experiment data on the mechanical responses of ice–soil mixtures to ice melting. A relationship between normalized volumetric change and normalized time was established to describe the progression of ice melting. The melting of ice particles caused significant deformation, increases in the void ratio and degree of saturation, and reductions in the shear strength. These properties reached a steady state when the initial ice content exceeded 30%.
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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) | 0.001 | 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".