A novel technique to monitor subsurface movements of landslides
Bibliographic record
Abstract
Slope deformation sensors (SDSs) were developed to monitor profiles of soil deformation at a high frequency during slope-monitoring and landslide-triggering experiments. It was hypothesized that the surface and subsurface movements could be combined to integrate the temporal development of the movements and help monitor the initiation and propagation of the shear bands indirectly, as well as predict the volume of the eventual landslide. Four SDSs were installed in a 38° slope in Northern Switzerland and slope movements due to two artificial rainfall sprinkling experiments in October 2008 and March 2009 were monitored. This paper describes the design, numerical validation, installation details, and performance of the SDSs during the first rainfall event. The data acquired from SDSs in terms of bending strain, deformation profiles, and an indication of the mechanical energy transmitted from the surrounding soil are analysed and compared with the patterns of surface movements of the slope and changes in the horizontal earth pressure. The findings are interpreted based on the applied rainfall, hydrological properties of the slope, bedrock shape, and specifications of the observed failure surface in the subsequent landslide triggering experiment. Details of the data acquired from SDSs during the second experiment in March 2009 are reported and analysed in a forthcoming paper.
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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 | 0.001 | 0.001 |
| 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".