Bayesian inversion of propagation and reverberation data
Bibliographic record
Abstract
A Bayesian matched-field inversion approach to infer geoacoustic and scattering properties of the seabed is applied to simulated propagation and reverberation data received on a towed horizontal array. The approach is based on the method of fast Gibbs sampling (FGS) of the posterior probability density to estimate uncertainties in both geoacoustic and scattering parameters for broadband acoustic data in realistic shallow-water environments. The FGS is linked to an acoustic propagation model that simultaneously provides complex acoustic pressure at short propagation ranges and long-range reverberation intensity. The inversion algorithm is initially applied to long-range reverberation data alone to assess the geoacoustic information content of reverberation in terms of marginal posteriori probability densities for the environmental parameters. A reduction in uncertainty for the extracted geoacoustic and scattering parameters is demonstrated by a simultaneous inversion of the propagation and reverberation horizontal array data.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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".