Long-term investigation of a deep-seated creeping landslide in crystalline rock. Part II. Mitigation measures and numerical modelling of deep drainage at Campo Vallemaggia
Bibliographic record
Abstract
For more than 200 years, the villages of Campo Vallemaggia and Cimalmotto have been slowly moving on top of a deep-seated landslide in the southern Swiss Alps. Numerous mitigation measures have been carried out during this time to stabilize the landslide but with limited to no success. Those attempts largely focussed on minimizing erosion at the toe of the landslide. More recently, the need to stabilize the slope began to intensify, as with each passing year the two villages were being pushed closer to the edge of a 100 m high erosion front at the foot of the landslide. This led to an extensive investigation and monitoring campaign to better understand the factors controlling the landslide movements, which as reported in Part I (see companion paper, this issue), pointed to high artesian pore pressures as being the primary destabilizing mechanism. Here in Part II, the arguments supporting the need for a deep drainage solution are reported, as is the history, implementation, and measured response of the Campo Vallemaggia landslide to the various mitigative measures taken. Numerical modelling results are also presented, based on hydromechanically coupled distinct-element models, to help demonstrate why deep drainage succeeded where other mitigation measures failed.
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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 | 0.001 |
| 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".