An Integrated InSAR-Borehole Inclinometer-Numerical Modeling Approach to the Assessment of a Slow-Moving Landslide
Bibliographic record
Abstract
ABSTRACT We use results of satellite-based interferometric synthetic aperture radar, Global Positioning System, and borehole inclinometer data to constrain numerical models that improve understanding of slope deformation at the Alexandria landslide, British Columbia, Canada. Surface monitoring data and borehole slope inclinometer measurements provide important insight into the slope failure mechanism. We initially analyzed the data in a geographic information system (GIS) to create thematic maps of the landslide area (hillshade, slope, and aspect), to identify key geological features, and to produce an engineering geomorphology map of the landslide. The monitoring data and the geological/engineering geomorphological models provide important constraints for two-dimensional landslide limit equilibrium and finite difference analyses. The initial limit equilibrium analysis improved understanding of the sliding surfaces. The finite difference models were then used to simulate and investigate the potential slope deformation mechanism. The combined slope monitoring/modeling results show that the Alexandria landslide is a slow-moving, multiple-block, retrogressive slope failure. The close agreement between the limit equilibrium and finite difference analyses, together with the satellite and ground-based slope monitoring and GIS data, highlight the importance of using a multidisciplinary/integrated approach in landslide studies.
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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 | 0.001 |
| 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 | 0.001 | 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".