A SPECTRAL DECOMPOSITION PROCEDURE FOR DETERMINING THE FIELDS AT THE BOUNDARIES IN A PE-BASED REVERBERATION MODEL
Bibliographic record
Abstract
Numerical predictions of the reverberation level (RL) in an ocean waveguide depend critically upon transmission loss (TL) calculations (for a recent compilation of available TL and RL models, refer to the online review article by Etter 1 ).TL models based on ray-theoretical 2,3 or normal-mode 4-6 representations of the pressure field have traditionally been used in this context.These codes provide four inherent capabilities: (1) direct computation of vertical arrival angles, (2) readily determined incident fields at the sea-surface and at the sea-bottom, (3) estimation of coherent (or incoherent) TL versus range characteristics via ray or mode summation, and, (4) an accommodation (albeit approximate) of range-dependent variations in medium properties (e.g., sound speed profiles and bathymetry).Note that arrival angle information together with incident TL values at boundaries are needed in order to apply appropriate angle-dependent sea-surface and sea-bottom scattering losses to the backscattered RL fields.Predictions of low-frequency TL in shallow water, however, can be challenging for ray-type codes, while range-dependent mode calculations can be computationally intensive except in the adiabatic regime (i.e., no mode coupling).In contrast, predictions based on high-order Padé approximations to the exact one-way parabolic equation (PE) propagator 7,8 can produce accurate and efficient estimates of TL at low frequencies in range-dependent situations where mode coupling is present.High-order PE models have been shown to accurately model forward scattering from a deterministic rough surface 9,10 as well as backscattering from step-like and sloping bathymetry.11
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.004 | 0.003 |
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".