Characterizing debris transfer patterns in the White Canyon, British Columbia with terrestrial laser scanning
Bibliographic record
Abstract
In the Thompson-Fraser Rail Corridor in Interior British Columbia, the Canadian National (CN) rail line traverses several alluvial fans, which are subject to occasional debris flows. Debris flows pose a significant geohazard due to the combination of high flow velocities, large impact forces, long runout distances and poor temporal predictability. When a debris flow occurs, the cost of repairs, maintenance, and construction along these single-track railway lines is compounded by the fact that these activities also impede the flow of rail traffic, which has financial repercussions for the operators. As a result, it is vital to be able to identify and prioritize the slopes that pose the greatest hazard to the rail lines. A thorough understanding of the geohazards present on site is an essential component of risk assessment. The Canadian Railway Ground Hazard Research Program (RGHRP) was established in 2003 with the aim of better understanding the natural hazards impacting railway operations across Canada. The present study is part of this initiative and focuses on an active site called the White Canyon, which is located 275 kilometers northeast of Vancouver, BC. In this study, we use terrestrial laser scanning (TLS) and panoramic imagery datasets to analyze the debris recharge patterns that develop between debris flows in a select channel in the White Canyon. TLS scans taken before and after the events provide insight into the volumes of material mobilized and how we can leverage this series of TLS data to give insight into the amount of debris accumulating in the channels prior to failure. The temporal data acquisition rate was found to have a significant influence on the amount of movement that can be interpreted from the TLS change detection analysis and panoramic images. Therefore, the temporal data acquisition rate is key consideration when using TLS to support the determination of accurate return periods on 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".