Bayesian source tracking in an uncertain ocean environment.
Bibliographic record
Abstract
This paper considers matched-field tracking of a moving acoustic source when properties of the ocean environment (water column and seabed) are poorly known. The goal is not simply to estimate source locations but to determine track uncertainty distributions, thereby quantifying the information content of the tracking process. To localize and track low-level sources, acoustic data collected for multiple time samples (corresponding to multiple source positions) are inverted simultaneously, with constraints included on the maximum allowable motion between samples. This increases the information content over sequential tracking approaches such as particle filtering, but also increases the dimensionality and difficulty of the inversion. A Bayesian formulation is applied in which the posterior probability density (PPD) is integrated over unknown environmental parameters to obtain a time-ordered sequence of joint marginal probability surfaces over source range and depth. Marginal PPDs are computed numerically using Metropolis–Hastings sampling over environmental parameters (rotated into principal components and applying linearized proposal distributions) and two-dimensional Gibbs sampling over source locations. The approach is illustrated using Mediterranean Sea data, and tracking information content is considered as a function of data quantity (number of time samples and frequencies processed), data quality (signal-to-noise ratio), and level of prior information on environmental parameters.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".