Landslide motion assessment including rate effects and thermal interactions: revisiting the Canelles landslide
Bibliographic record
Abstract
The re-activation of a large (40 Mm3) landslide on the valley slopes of a reservoir motivated a research initiative to estimate the risk of a fast-sliding mass moving into the reservoir. A previous simplified analysis had suggested that a joint consideration of strain rate effects on friction and thermal pressurization phenomena in the sliding surface could provide a rational approach to answer the question raised. The paper describes first the capability of strain rate effects on friction to reproduce long-term creeping records of two real cases. The joint and coupled phenomena of creeping motion and thermal pressurization in shearing bands was incorporated into a material point method computational technique for hydromechanical analysis of porous materials. A representative cross section of the Canelles landslide was then analysed, profiting from previous finite element investigations of the landslide. It was found that a rapid rate of landslide acceleration could be a possibility under extreme external actions. However, it was also found that a moderate strain rate effect on the basal residual friction angle could create conditions that avoid the triggering of a fast motion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".