Change Detection and Feature Extraction of Debris‐Flow Initiation by Rock‐Slope Failure Using Point Cloud Processing
Bibliographic record
Abstract
Our understanding of debris‐flow initiation by slope failure is restricted by the challenge of acquiring accurate geomorphic features of debris flows and the structural setting of the rock mass in the remote mountainous terrain. Point cloud data of debris flows in Sabino Canyon, Tucson, Arizona, July 2006, with initiation by joint‐controlled rock slope were obtained using multitemporal LiDAR scanning. Topographic changes were detected by comparing historical LiDAR scanning data of this area since 2005 by adopting open‐source CloudCompare software. The results showed persistent scour and erosion in the debris flows after 2006. Point cloud data of joint‐controlled rock in the initiation zone were generated by the means of photogrammetry using Pix4D software. The joint planes, the dip direction and the dip value of the joint plane, the joint spacing, and the joint roughness were therefore acquired by point cloud processing. Our study contributes a foundation for analyzing the relationship between the rock features, the generation of slope failure, and the initiation of debris flows.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".