Managing rockfall risk through baseline monitoring of precursors using a terrestrial laser scanner
Bibliographic record
Abstract
Rockfalls represent significant risks to safe and efficient use of transportation corridors. In this paper, we address the management of rockfall risk through baseline remote monitoring of susceptible slopes (every 2–4 months) along a transportation corridor along the Fraser River valley in western Canada using a terrestrial laser scanner and supporting remote sensing technologies. This includes identifying potential rockfall source zones based on incipient signs of failure, tracking kinematics in three dimensions to better understand the mechanism of failure, estimating potential failure volumes based on bounding joint structure, and transmitting this information to the railway operator for an assessment of risk. We demonstrate our approach for one case along the line where we identified several potential failures ranging in volume from 48 to 4200 m3. Our projections of the location of failures were successful, in that volume projections were within 10%–55%, and the anticipated kinematics and failure mechanism were consistent with the assessment of post-failure rockfall scar geometries. Accurate volume and kinematics estimates are important for the assessment of hazard and risk as well as the planning of risk mitigation options. In general, this approach can be used to better manage risk from rockfall hazard in communities, transportation corridors, or other infrastructure.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".