A hands-on approach to estimate debris flow velocity for rational mitigation of debris hazard
Bibliographic record
Abstract
Rational estimation of debris flow velocity is one of the key issues in debris hazard mitigation. Among the various procedures, back-calculation of debris flow velocity is one of the most frequently used approaches. Back-calculation includes determination of super-elevations and channel properties, and velocities are calculated using the forced vortex equation. Super-elevation and bend radius are biasing parameters in the back-calculation scheme. Therefore, an iterative approach based on simplified assumptions is developed to avoid the direct and subjective determination of bend radius. Besides, as only the highest mud prints are visible after the disaster, this misreads the actual super-elevation. To seek better ways to fix this anomaly, a series of three-dimensional numerical curved flume tests using smoothed particle hydrodynamics are carried out. Estimated velocities from highest flow marks underestimate the actual velocities near the source, while they converge on the actual velocities as the distance to source increases. A best-fit line is then proposed to adjust the mud-marks-derived velocities to the real velocities. The assumption taken during development of the iterative approach for determining bend radii is also justified from the numerical simulations. The law of similarity allows application of these findings to real debris flow disasters. Thus, two real debris flow disasters in Japan are examined for validation of the developed procedure and adjusted velocities are proven to be consistent with verbal evidences and previous analyses.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".