Toward benchmarks of low frequency reverberation level in a Pekeris waveguide: Insight from analytical solutions.
Bibliographic record
Abstract
The requirement by modern navies to predict sonar performance in shallow water, whether for use in research, planning, or operations, led to an initiative for the validation of reverberation models in the form of two reverberation modeling Workshops at the University of Texas at Austin [J. S. Perkins and E. I. Thorsos, J. Acoust. Soc. Am. 126, 2208 (2009)]. The scenario considered here (Problem XI, from the first workshop) requires the computation of reverberation versus time in a Pekeris waveguide with Lambert scattering from the seabed. Simple analytical methods are presented that provide insight into the dominant propagation paths giving rise to the reverberation and hence establish regimes of validity for various computer models making different approximations or assumptions. Results from eigenray, normal mode, and hybrid continuum methods are compared with each other and with the analytical solutions. Numerical predictions are shown to overlap to within a few tenths of a decibel in regions where the different assumptions made by the various models are valid. These overlapping solutions are proposed as “benchmarks” in the sense of a baseline against which future model improvements can be assessed and quantified.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".