A Matlab and normal mode based reverberation model and some data/model comparisons
Bibliographic record
Abstract
A Matlab and normal mode based reverberation model has been developed that uses Ellis’ algorithm [J. Acoust. Soc. Am. 97, 2804–2814] for reverberation predictions and the ORCA normal mode model [Westwood et al., J. Acoust. Soc. Am. 100, 3631–3645] to compute the eigenvalues and mode functions. The model is currently range independent, but handles bistatic geometries, and towed array beam patterns. Group velocity corrections similar to LePage’s [J. Acoust. Soc. Am. 106, 3240–3254] have recently been added to this model. The matrix formulation in Matlab allows the bistatic calculations to be performed more efficiently. An overview of the model is presented that includes sample model-model comparisons for some problems from the 2006 ONR Reverberation Workshop. The model predictions are also compared with data obtained with the NURC (formerly the NATO Undersea Research Centre, SACLANTCEN) towed arrays during several reverberation experiments on the Malta Plateau. Select data from these experiments are used to compare with model predictions using best estimates of bottom properties in the area. [Work supported by US Office of Naval Research, Code 321OA, Grant No. N00014-05-1-0156.]
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".