Characteristics of large landslides in sensitive clay in relation to susceptibility, hazard, and risk
Bibliographic record
Abstract
The clay plains of the Saint Lawrence Lowlands of eastern North America are subject to large landslides in sensitive clay. These landslides occur relatively infrequently, but can have very significant consequences. This type of risk (low frequency, high consequence) can be difficult to manage, as the return period is long enough that the most recent major event tends to be forgotten by the time the next major event occurs. This paper examines characteristics of large landslides in sensitive clay with the purpose of understanding the nature of the hazard, and this work is extended to develop a high level appreciation of risk to a network of linear infrastructure, using railways as an example. The analysis considers the characteristics of a number of large landslides documented in the literature, as well as statistical characteristics of a digital inventory of large landslides, including: surface area, debris travel distance, retrogression length, proximity to other landslides, crater shape, landslide mechanism, temporal frequency, and documented effects. A linear network with between 100 and 1000 river crossings in the sensitive clay deposits in eastern Canada is expected to suffer a major disruption once every 10 to 100 years.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".