Bibliographic record
Abstract
The problem of estimating seabed geoacoustic parameters from ocean acoustic measurements has received considerable attention in recent years. Geoacoustic inversion represents a convenient alternative to direct measurements (e.g., coring) and provides sensitivity relevant to acoustic source localization applications; however, it requires solving a strongly nonlinear inverse problem. A variety of approaches have been developed (by a number of researchers) based on seeking geoacoustic parameters that provide the optimal match to measured acoustic fields using global search techniques. Other approaches include inversion of bottom-loss or seabed-reflectivity data and ambient noise. Topics of current interest include range-dependent inversion, coherent spatial/temporal processing, and uncertainty estimation. This paper reviews the above approaches in terms of a general probabilistic formulation for geoacoustic inversion. The goals of the probabilistic approach are to fit the acoustic data and available prior information to within their uncertainties, and to estimate geoacoustic parameters, their uncertainties, and inter-relationships. This is accomplished using a Bayesian formulation and Markov chain Monte Carlo approach (Gibbs sampling) to extract features of the posterior probability density such as the maximum a posteriori estimate, marginal probability distributions, and correlations. The approach is illustrated for matched-field inversion, inversion of seabed reflectivity, and source localization with environmental uncertainty.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".