Bayesian inversion of seabed scattering data.
Bibliographic record
Abstract
Reverberation modeling and sonar performance predictions in shallow water require good estimates of seabed scattering and reflection as well as an understanding of scattering processes in a particular region. This talk considers the ability to resolve scattering parameters (e.g., scattering strength and roughness) and geoacoustic parameters (layer thicknesses, sound speed, density, and attenuation) using Bayesian inversion and a forward model based on first-order perturbation scattering theory and multilayer reflection coefficients. Results are considered in terms of marginal posterior probability distributions, which quantify the effective data information content to resolve scattering/geoacoustic parameters. Inversions are applied to synthetic data and to direct-path scattering measurements from shallow-water test beds. These measurements probe the seabed on an intermediate spatial scale (patch-size radius of ∼500 m for both reflection and scattering), which reduces the effects of ocean variability (associated with sound speed profile, seabed, and biologics) and uncertainty relative to long-range reverberation measurements. [This work is supported by the 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".