Examining meso-scale variability: Sequential geoacoustic posterior probability estimation for autonomous underwater vehicle (AUV) towed array data
Bibliographic record
Abstract
This paper applies a sequential trans-dimensional Monte Carlo method to data recorded on an autonomous underwater vehicle towed array. The seismo-acoustic recordings are processed in terms of reflection coefficients as a function of frequency and angle, resulting in a sequence of data sets with small seabed footprints. The sequential particle filter applies advanced Markov chain Monte Carlo steps in combination with importance sampling techniques to carry out consecutive geoacoustic inversions along the track. In particular, the typically high information content of reflection-coefficient data is addressed by heuristic interpolating distributions implemented using annealed importance sampling to bridge between adjacent data sets with distant/disjoint high-likelihood regions. The geoacoustic parametrization, including changes in the number of sediment layers, is estimated along the track with trans-dimensional partition modeling. This trans-dimensional approach intrinsically estimates the amount of structure consistent with the data-information content and accounts for the limited knowledge about the parametrization in the geoacoustic posterior probability density estimates. The algorithm is applied to a sequence of data sets collected on the Malta Plateau with small seabed footprints (~10 m), which facilitates examination of meso-scale seabed variability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".