PE solutions to some internal-wave benchmark problems
Bibliographic record
Abstract
Benchmarking underwater acoustic propagation models is a necessary stage in the evolution of numerical modeling codes. Candidate benchmark problems should challenge the capabilities of existing models in order to promote the development of improved techniques for reliably simulating sound propagation in realistic oceans. In this paper, a parabolic equation (PE) model is applied to the suite of internal-wave test cases that are offered for numerical consideration by the organizers of the Benchmarking Range Dependent Numerical Models session. The PE calculations are carried out using a code that was originally developed for matched-field processing applications [G. H. Brooke et al., ‘‘PECan: A Canadian parabolic equation model for underwater sound propagation,’’ J. Comput. Acoust. (2001)]. The capability of PECan to propagate sound accurately through the benchmark environments is examined for both tonal and broadband signals. The discussion will address issues related to reciprocity, interpolation of the internal-wave sound speed structure, and convergence of solutions as a function of Padé order and range and depth step sizes. Where possible, PECan will be validated against other well-known propagation models to assess its accuracy as well as to appraise the suitability of the proposed internal-wave benchmark problems to test the limits of existing propagation codes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".