GIS-based modeling of snowmelt-induced landslide susceptibility of sensitive marine clays
Bibliographic record
Abstract
Abstract In this study, a simulation tool, based on the combination of Geographic Information System (GIS) and Grid-Based Regional Slope-Stability Model (TRIGRS), is developed to assess and predict the snowmelt-induced landslides in areas of sensitive marine clays in the Ottawa region (Canada). Topographic, geologic, hydrologic, and geotechnical information of the study area, in addition to snowmelt intensity data for different periods, was required to conduct this modeling study. Snowmelt intensity records for periods of 6–48 h, 3–15 days, 25 days, and 30 days, as well as the information on historical landslides in the study area, were used to examine both the timing and location of shallow landslides due to snowmelt across the Ottawa region. The developed tool is validated by comparing the predicted landslide-susceptible areas with the historical landslide maps in the study area. A good agreement between the predicted and recorded historical landslides was obtained, which suggests that the developed GIS-TRIGRS based tool can predict relatively well the snowmelt-induced landslide susceptibility in the sensitive marine clays. The modeling results show that high slope areas of sensitive marine clays are more prone to snowmelt-induced landslides. As a result, this simulation tool could be used to assess and/or predict snowmelt-induced landslides in different areas of the Ottawa region, which is an important means for decision-making processes with respect to protecting the population and infrastructure against snow-melt induced marine clay landslides.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".