An Enhanced Framework to Quantify the Shape of Impulse Waves Using Asymmetry
Bibliographic record
Abstract
Abstract The shape of a wave generated by a landslide, snow avalanche, or fluid flow greatly influences its size and speed as it propagates away from the source region, which are critical parameters needed to estimate the impacts of these waves on coastal communities. In this study, laboratory data are produced from waves generated by the impact of water into a wave flume akin to the impact of a fluidized, highly mobile, and neutrally buoyant slide into a reservoir. Water surface observations are made using wave probes that remain at fixed positions, while the water depths and source volumes of slide material are varied. The wave shape is quantified by calculating the asymmetry about the vertical axis at each wave probe. The experimental results indicate that waves with positive or near‐zero asymmetry in the near field have a small influence on the maximum wave amplitude along the flume. However, waves with negative asymmetry in the near field change rapidly in shape and amplitude due to breaking until a stable state with symmetrical shape and wave breaking limit of 0.6 is reached. The length scale at which the breaking waves reach this state is quantified based on the initial asymmetry. An enhanced mathematical framework is developed using horizontal‐scale coefficients to modify the solitary wave equation such that it can be used to generate asymmetrical waves. This new method might be used in combination with predictions of the maximum wave amplitude to create time series needed to account for the shape of the tsunamis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".