3-Dimensional Rockfall Shape Back-Analysis: Methodsand Implications
Bibliographic record
Abstract
Abstract. Rockfall is a complex natural process that can present risks to the effective operation of infrastructure in mountainous terrain. Remote sensing tool and techniques are rapidly becoming the state of practice in the characterization, monitoring and management of these geohazards. The aim of this study is to address the methods and implications of how the dimensions of 3-dimensional rockfall objects, derived from sequential terrestrial laser scans (TLS), are measured. Previous approaches are reviewed, and two novel algorithms are introduced in an attempt to standardize the process. The approaches are applied to a set of synthetic rockfall objects generated in the open-source software package Blender. In addition, a database of close to 5000 rockfalls is presented derived from sequential TLS monitoring in the White Canyon, British Columbia, Canada. This study illustrates that the method in which the rockfall's dimensions are calculated has a significant impact on how the shape of a rockfall object is classified. This has implications for rockfall modelling as the block shape is known to influence rockfall runout.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".