Bibliographic record
Abstract
Abstract. Debris flows affect people and infrastructure around the world, and as a result, many numerical models and modelling approaches have been developed to simulate their impacts. Observations from instrumented debris-flow channels show that variability in inflow depth, velocity, and discharge in real debris flows is much higher than what is typically used in numerical simulations. However, the effect of this natural variability on numerical model outputs is not well known. In this study, we examine the effects of using complex inflow time series within a single-phase runout model utilizing a Voellmy flow-resistance model. The interactions between model topography and flow resistance were studied first using a simple triangular hydrograph, which showed that simulated discharges change because of local slopes and Voellmy parameters. Next, more complex inflows were tested using time series based on 24 real debris-flow hydrographs initiated from three locations. We described a simple method to scale inflow hydrographs by defining a target event volume and maximum allowable peak discharge. The results showed a large variation in simulated flow depths and velocities arising from the variable inflow. The effects of variable-inflow conditions were demonstrated in simulations of two case histories of real debris flows, where the variation in inflow leads to significant variations in the simulation outputs. The real debris-flow hydrographs were used to provide an indication of the range of impacts that may result from the natural variability in inflow conditions. These results demonstrate that variation in inflow conditions can lead to reasonable estimates of the potential variation in impacts.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.027 | 0.021 |
| Insufficient payload (model declined to judge) | 0.137 | 0.106 |
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".