Applications of New Remote Sensing Technology to Natural Rock Slope Stability Analysis
Bibliographic record
Abstract
Assessment of stability of natural rock slopes located above infrastructure corridors, such as railways, and highways is required in order to evaluate potential for failure, loss of infrastructure capacity, and threats to public safety. As these slopes are not engineered, natural failure processes dominate. These are difficult to assess as direct physical access is generally not possible, and monitoring data is rarely available. A variety of remote sensing techniques from several vantage points and platforms have been deployed to evaluate rock slope stability, including LiDAR, photogrammetry, and Gigapan photography, from terrestrial and a variety of airborne platforms. Techniques have been developed to utilize both single data acquisitions as well as time sequential data sets. Analysis of data sets from different sources has proven to be useful in order to reduce loss of data due to occlusion and to provide different data types which are useful for different applications. A number of case histories of large rock slopes in the mountainous areas of western Canada will be used to demonstrate the integration of engineering geology into the remote sensing analysis of the rock slopes. Research products include assessment of the rockmass characteristics, an assessment of block volume, analysis of slope deformation and failures leading to forecasting of potential future events, dependent on the failure mechanism. As the database of rock slope case histories continues to build, we are moving ever closer to a more detailed understanding of rock slope failure modes, precursor events, deformation thresholds and the effect of triggering events.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".