Bibliographic record
Abstract
A relatively simple and versatile numerical model known as DAN, developed by Oldrich Hungr for the assessment of landslide mobility, has been enhanced to incorporate the consideration of a trapezoidal-shaped flow channel for more realistic modelling purposes. This enhanced debris mobility model (DMM) has been programmed to run on a spreadsheet and has been calibrated against detailed landslide data in Hong Kong. In contrast to previous work, the enhanced DMM eliminates the limitations inherited from the assumption of a rectangular flow channel with frictionless side boundaries. When using DMM, no predefined width of landslide is needed. The DMM simulates landslide mobility with the flow resistance on the whole wetted perimeter of the channel, calculates the surface width of the landslide based on the cross-sectional geometry of the channel, and is capable of predicting a lobe-shaped debris deposition area. This technical note presents details of the enhancement to the DAN formulation.Key words: landslides, debris flows, runout analysis, landslide mobility, dynamic modelling, numerical methods.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".