Methods for predicting the occurrence and post-failure characteristics of retrogressive failures in sensitive clays: Evaluation of their applicability in Eastern Canada
Bibliographic record
Abstract
• Evaluation of the applicability of different methodologies through a catastrophic flowslide. • Only Norwegian methodologies are applicable for failure-type prediction. • Limited applicability of Swedish methodology in Eastern Canada. • Identification of predictors for a potential failure-type prediction method in Eastern Canada. • Potential areas of improvement for Eastern Canadian sensitive clay landslide management. Retrogressive landslides in sensitive clays pose significant risks in Scandinavia and Eastern Canada due to their rapid strength loss and extensive debris flow. While regional hazard management strategies exist, they differ significantly in their approaches to predicting failure types and post-failure characteristics. Notably, only Norwegian methodologies currently include a procedure for failure-type prediction. This paper aims to provide a comprehensive evaluation of these methodologies from Quebec, Norway, and Sweden, including their advantages and limitations. Their applicability to the Eastern Canadian context is also assessed through a case study of the 1971 Saint-Jean-Vianney flowslide. The results of the case study show that two of the Norwegian methods handled this event quite effectively in terms of retrogression distance, with results that are within about 5% of the actual distance. The method used in Eastern Canada projected a retrogression distance of 80 m, which is significantly lower than the observed 600 m. Predicting runout remains challenging; the Norwegian empirical limit underestimated the actual runout by approximately 34%. The case study suggests that the methodology used in Eastern Canada may necessitate separate datasets for estimating retrogression distance on opposite banks of the same river, although further investigation is required. Additionally, developing and incorporating a failure-type prediction procedure could improve its effectiveness as a hazard management tool.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 |
| 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 teacher head, 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".