Nonlinear geoacoustic inversion via parallel tempering
Bibliographic record
Abstract
The method of parallel tempering is applied to achieve efficient and effective sampling of a particularly challenging multi-modal problem involving the inversion of acoustic reverberation data for geoacoustic and scattering parameters. Metropolis-Hastings sampling (MHS) and parallel tempering are compared for Bayesian geoacoustic inversion of simulated (noisy) reverberation data. A range-independent seabed model is assumed for the reverberation inversion problem in which the seabed is represented by an upper sediment layer of thickness 5m, sound velocity 1470 m/s, density 1.4 g/cm3, and attenuation 0.5 dB/wavelength. The standard deviation of the data errors 1dB is also considered an unknown parameter in the inversion. Considering the parallel-tempering results, it is found that the multi-modality of the joint marginals are mapped out far better using parallel tempering samples using MHS. There is little practical difference in results for different there is little practical difference in results for different.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".