Characterization of soil composition and calculation of genetic mechanism in the landslide zone of Liujiabao landslide in Three Gorges reservoir area
Bibliographic record
Abstract
The Three Gorges Reservoir Area is a hotspot for landslide disasters, with many landslide development patterns and influencing factors remaining unclear.The slip zone soil, a weak interlayer between the sliding mass and the bedrock, has inherently low strength, which is a critical factor in landslide occurrence.Water is one of the most active elements reducing the shear strength during the formation of the slip zone.Given the particularity of reservoir bank water-related landslides, the mineral composition and geochemical characteristics of the slip zone and its surrounding rocks and soils exhibit significant variations across different geological periods and environments.These changes reveal the mechanisms and extent of water-rock interactions, further clarifying the fundamental reasons for the reduction in shear strength of the slip zone.The results show that in the Liujiaobao landslide in Quchi Township, Wushan County, Chongqing, within the Three Gorges Reservoir area, the composition of minerals and the content of major chemical elements in the slip zone soil and its surrounding rocks and soils indicate that the slip zone and surrounding rocks and soils form the material basis for the slip zone.During its formation, the groundwater in the slip zone is closely connected with external hydraulic forces, continuously influenced by groundwater, leading to changes in the physical properties of the rock and soil mass.This is accompanied by the hydrolytic mudification of marl debris, dissolution of calcite, and interconversion among clay minerals, which are the main reasons for the attenuation of the shear strength of the slip zone soil.
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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 | 0.002 | 0.002 |
| 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) | 0.000 | 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".