Simulation study of joint trans-dimenstional bayesian inversion of scattering and reflection data
Bibliographic record
Abstract
A joint trans-dimensional (trans-D) Bayesian inversion approach is developed and is applied to synthetic seabed scattering and reflection data to determine the ability of such data to resolve geoacoustic and scattering parameters. Bayesian inversion requires specifying the posterior probability density (PPD), and a method to sample PPD. To adequately approximate the PPD 600,000 models were sampled from it using the rjMCMC algorithm. These are thinned by one third to reduce sample correlation; only these remaining samples are considered here. The one- and two-dimensional marginal distributions of the scattering parameters for the inversion are found. The parameter distributions are found to be centered near the true values, and the uncertainties indicate a useful level of resolution of the roughness spectrum. Geoacoustic parameters are also well resolved, but are not shown here due to space constraints.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".