A comparison of ray, normal-mode, and energy flux results for reverberation in a Pekeris waveguide
Bibliographic record
Abstract
A number of problems were developed for, and presented at, a 2006 Reverberation Modeling Workshop sponsored by the US Office of Naval Research. The simplest of these known to the participants as Problem 11, was a Pekeris waveguide (isospeed water over a flat bottom half-space) with Lambert bottom scattering. The water depth was 100 m and frequencies of 250, 1,000, and 3,500 Hz were specified. A number of source-receiver combinations were specified, but the reverberation predictions are quite insensitive to sensor depth except at 250 Hz. With some benefit from hindsight and the results from other models, we compare our results from ray, normal-mode, and energy-flux approaches. All three approaches agree at intermediate times, say 3 to 50 s. At short times, the steep-angle paths and fathometer returns cause the mode and energy-flux models to underpredict the reverberation. At longer times, the ray models run out of steam: i.e., there are too many contributing ray paths for them to handle so they underpredict the reverberation. By combining the model predictions together with analytical results from an energy-flux model, we propose a composite benchmark solution. [Work supported in part by ONR.]
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".