Tempered particle filters for non-linear model selection and uncertainty quantification of highly informative seabed data
Bibliographic record
Abstract
Knowledge about seabed properties is important for many geoscientific and navy applications, such as sediment transport, sonar performance prediction, and detection of unexploded ordnance. Bayesian model selection and uncertainty estimation have been shown to provide detailed, quantitative seabed knowledge that is valuable for these applications. However, the extreme computational cost limits the utility of Bayesian methods for increasingly common big data sets. Here, we consider geoacoustic reflectivity surveys based on towed source and receiver arrays. Such systems produce thousands of data sets with high information content that require non-linear inversion along tracks many kilometers in length and cannot be analyzed by standard Bayesian sampling. A particle filter that includes reversible jump Markov chain Monte Carlo updates is applied here for efficient posterior probability estimation. Efficiency is improved by likelihood tempering of various particle subsets and including information exchange within the particle cloud. The tempering applies to reversible jump updates and leads to significantly improved exploration of the trans-dimensional seabed model which accounts for changes in the number of sediment layers and their properties along the track. For challenging track sections, where data change abruptly, the particle cloud is resampled to increase the number of tempered particles. [Work supported by the U.S. Dept. of Defense, through SERDP, and by ONR Ocean Acoustics.]
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".