Insights into the shallow landslide mechanism of expansive soil slope induced by freeze–thaw cycles and snowmelt infiltration
Bibliographic record
Abstract
For rigorous understanding the shallow landslide mechanisms and deformation characteristics of expansive soil slopes, a comprehensive in-situ monitoring platform is established. Triaxial creep tests and microstructure analysis with scanning electron microscopy are also conducted on expansive soil samples obtained from Binxi station. Field monitoring data indicates that freeze–thaw (F–T) cycle and snowmelt infiltration significantly increase the creep deformation of expansive soil slope during spring melting period. Due to the influence of F–T cycle and snowmelt infiltration, more soil grains are involved in the shear deformation contributing to a large, localized shearing. Additionally, the microstructural analysis shows that F–T cycle influences the relationship between expansive soil grains that gradually change from face–face contact to point–face contact or edge–edge contact form. The shallow landslide mechanisms of expansive soil slope are revealed from creep deformation and microstructure characteristics of soils after the F–T cycle and snowmelt infiltration, which can be summarized into two stages, namely, the snowfall accumulation state and snow melt-shallow infiltration stage. These results can serve as a good reference for the prevention of expansive soil slopes in seasonally frozen regions.
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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".