Bayesian localization of multiple ocean acoustic sources with environmental uncertainties
Bibliographic record
Abstract
This paper considers sim ultaneous localization o f multiple acoustic sources w hen properties o f the ocean environment (water colum n and seabed) are poorly known [1, 2].A Bayesian formulation is applied in w hich the environmental parameters, noise statistics, and locations and com plex strengths (amplitudes and phases) o f multiple sources are considered unknown random variables constrained by acoustic data and prior information.The posterior probability density (PPD ) over all parameters is defined and integrated using efficient M arkov-chain Monte Carlo methods to produce joint marginal probability densities for source ranges and depth.This approach also provides quantitative uncertainty analysis for all parameters, w hich can aid in understanding the inverse problem and may be o f practical interest (e.g., source-strength probability distributions).Closed-form m axim um -likelihood expressions for source strengths and noise variance at each frequency (developed in the follow in g section) allow these parameters to be sampled im plicitly, substantially reducing the dimensionality and difficulty o f the inversion.An exam ple is presented o f multiple-source localization in an uncertain shallow-water environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".