The Role of Extreme Precipitation in Triggering Mass Wasting Events in the St. Lawrence Lowlands: A Retrospective Analysis
Bibliographic record
Abstract
In Canada, landslide and mass wasting events are most associated with the mountainous regions of British Columbia and Alberta, where they have resulted in 356 fatalities (Government of Canada, 2024). Therefore, a great deal of landslide research focuses on Western Canada. However, despite experiencing significant mass wasting events resulting in 239 fatalities, Quebec appears to receive less attention in such research (Government of Canada, 2024). Within the St. Lawrence Lowlands, a region extending from Quebec City to the Ottawa Valley, numerous mass wasting events have destroyed communities, highlighting the need for more attention. Mass wasting events are complex and can be triggered by several factors, including extreme weather. With the possibility of climate change increasing the intensity and frequency of extreme weather events, understanding weather’s role in triggering mass wasting events in the St. Lawrence Lowlands is critical. This study examines the extent to which extreme weather, particularly heavy precipitation, has influenced past mass wasting events in the St. Lawrence Lowlands. A retrospective analysis is used by correlating past landslide and mass wasting events with historical weather data. By using public historical weather station data published by the Government of Canada, precipitation data is compared to the timing of mass wasting events. Additionally, the region’s soil compositions, particularly Leda clay, are analyzed to assess its susceptibility to rain-induced failure. It is expected that examining this relationship will explain the prevalence of mass wasting events in the region and the implications highlight potential future risks to communities amid a changing climate. Government of Canada (2024). NaturalResources Canada. Government of Canada. https://natural-resources.canada.ca/stories/simply-science/canadian-fatal-landslides-mapped
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".