Regional-Scale Landslide Hazard Analysis in Sensitive Clays Using an Integrated Approach
Bibliographic record
Abstract
The Saguenay region in Quebec is largely covered by sensitive marine clays that are highly susceptible to large retrogressive landslides.This work addresses the development of a methodology to forecast slope failure and zonation of landslides in sensitive clays.This is demonstrated by applying an integrated approach to the Saint-Jean-Vianney (SJV) area located in Saguenay.Developing the 3D model, assessing geotechnical parameters through back analysis using the Rocscience-Slide software, and creating a risk zonation map are the three crucial phases of the proposed methodology.The first phase involved generating a 3D geological model of the surficial deposits of the selected area using borehole data and geophysical methods.In the second phase, realistic values of geotechnical parameters of the soil were obtained by performing a back analysis of a previously occurred landslide in the area of interest and combining it with experimental results from clay specimens retrieved from the same landslide.In the final phase, a zonation map of slopes is created using the derived geological model and geotechnical factors to assess the slopes at risk.This screening tool enables the identification of high-risk zones that would require a more detailed investigation.The proposed approach provides a useful tool for slope stability analysis in areas with similar geological conditions.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".