Geoacoustic inversion using seabed reflection-coefficient data variations across frequency
Bibliographic record
Abstract
Seabed reflection-coefficient (RC) data as a function of grazing angle and frequency provide rich information content to estimate seabed sediment layering structure and associated geoacoustic properties. To date, Bayesian inversions of wide-angle, broadband RCs have provided high-resolution geoacoustic profiles and uncertainty estimates using data sets based primarily on the Bragg resonance pattern across closely-spaced angles at a small number of frequencies. This paper considers instead the use of RCs across closely-spaced frequencies (including magnitude variations and fringe patterns), which could potentially provide higher information content on certain geoacoustic properties, such as attenuation and its frequency dependence. To investigate this, inversions are carried out based on RCs as a function of frequency from 1–6 kHz at a small number of grazing angles (multiple angles are required to overcome the ambiguity between layer sound speed and thickness). Inversions are evaluated and compared for specific choices of grazing-angle regime, including low angles, high angles, and near the critical angle. [Work supported by the Office of Naval Research.]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".