Inter-Model Comparison for Tsunami Debris Simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Assessing the risk of tsunami-driven debris has increasingly been recognized as an important design consideration. The recent ASCE/SEI7-16 standard Chapter 6 requires all the areas included within a 22.5° spreading angle from the debris source to consider the debris impact. However, it would be more reasonable to estimate the risks using numerical simulation models. Although a number of simulation models to predict tsunami debris transport have been proposed individually, comparative studies for these simulation models have rarely been conducted. Thus, in the present study, an inter-model comparison for tsunami debris simulation model was performed as a part of the virtual Tsunami Hackathon held in Japan from September 1 to 3 in 2020. The blind benchmarking experiment, which recorded the transport of three container models under a tsunami-like bore, was conducted to generate a unique dataset. Then, four different numerical models were applied to reproduce the experiments. Simulated results demonstrated considerable differences among the simulation models. Essentially, the importance of accurate modelling of a flow field, especially a tsunami front, was confirmed to be important in simulating debris motion. Parametric studies performed in each model and comparisons between different models also confirmed that a drag coefficient and inertia coefficient would influence the simulated debris trajectory and velocity. It was also shown that two-way coupled modelling to express the interaction between debris and a tsunami is important to accurately model the debris motion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it