Bayesian Acoustic Source Track Prediction in an Uncertain Ocean Environment
Bibliographic record
Abstract
This paper develops an approach for probabilistic prediction of the future locations of a moving ocean acoustic source based on probability distributions for past source locations as determined by Bayesian acoustic tracking inversion. The Bayesian track estimation for past times considers both source and environmental parameters as unknown random variables constrained by noisy acoustic data and prior information, and numerically samples the posterior probability density (PPD) using Markov-chain Monte Carlo (MCMC) methods. Applying a probabilistic prediction model for constant-velocity source motion to each of the PPD samples produces source location probability distributions for future times. These prediction distributions account for both the uncertainty of the source-motion model and the uncertainty in the state of knowledge of past source locations including the effects of environmental uncertainty. Results of Bayesian track estimation and prediction are represented as a sequence of joint marginal probability distributions over source range and depth, and as the most probable track with uncertainties. Probability distribution for the time and range of the closest point of approach (CPA) are also computed for inbound tracks. The approach is illustrated with synthetic acoustic data at two noise levels and with measured data from a shallow-water site in the Mediterranean Sea.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".