Rock Slopes Asset Management: Selecting the Optimal Three-Dimensional Remote Sensing Technology
Bibliographic record
Abstract
Transportation corridors are classified as critical infrastructure in the United States and Canada. Successfully maintaining these corridors in safe, operational condition requires strategies that address individual hazard classes and manage associated risks. Natural and constructed slopes along transportation corridors represent one such category of hazard; typical risk management strategies involve quantitative measurements and qualitative evaluations of their present condition and the hazards that they pose to infrastructure, people, and shipments along the route. At some sites, traditional field-based observations are supplemented by high-resolution remotely acquired three-dimensional (3-D) imaging data of the ground surface, generated from various sensors and platforms, including terrestrial lidar and photogrammetry, airborne lidar, and oblique aerial photogrammetry. The collection, processing, and implementation of 3-D data collection and analysis into a slope management system are complex and frequently result in poorly collected, poorly understood, and underused data. Furthermore, the intricacies of the applications and limitations of different technologies generally are well understood only by specialists. As a result, the industry is reluctant to implement these technologies in active slope management systems. Practical procedures for remote data collection are illustrated, and the applications and limitations of the previously mentioned technologies are explained. The current capabilities of these technologies are presented; because the field is advancing rapidly, innovation and development soon will enhance the applicability of these technologies.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".