A process to simulate GNSS-R delay-Doppler map of tsunami-dominant sea surface
Bibliographic record
Abstract
In this paper, a process is presented to simulate Global Navigation Satellite System-Reflectometry (GNSSR) delay-Doppler maps (DDMs) of a tsunami-dominant sea surface. In this method, the bistatic scattering Z-V model, the sea surface mean square slope model of Cox and Munk and the tsunami-induced wind perturbation model are employed. By taking advantage of the first two models, the DDMs of tsunami-free region can be simulated. In order to accomplish the DDM simulation of tsunami-dominant surface, the tsunami-induced wind perturbation model is utilized on top of that. The simulations of the scattering coefficient distribution and the corresponding DDMs of a fixed region of interest before and during the tsunami are exhibited. On the final stage of analysis, by subtracting the simulation results that are free of tsunami from simulations with presence of tsunami, the tsunami-induced variations can be clearly observed. The process is implemented based on the 2004 Sumatra-Andaman tsunami.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".