Resolving spatial seabed variability by Bayesian inference of seabed reflection inversions.
Bibliographic record
Abstract
This paper considers Bayesian inversion of seabed reflection-coefficient data for multilayer geoacoustic models at several sites with the goal of studying spatial variability of the seabed. Rigorous uncertainty estimation is of key importance to resolve spatial variability between measurement sites from the inherent inversion uncertainties. Geoacoustic uncertainty estimation is carried out including Bayesian model selection and comprehensive estimation of data error statistics. Model selection is addressed using the Bayesian information criterion to ensure parsimony of the parametrizations. Data error statistics are quantified by estimating full covariance matrices from data residuals, and a posteriori statistical validation is carried out. A Metropolis-Hastings sampling algorithm is used to compute posterior probability densities. Five experimental sites are considered along a track located on the Malta Plateau, Mediterranean Sea, and the inversion results are compared to cores and subbottom profiler sections. Differences between sites that exceed the estimated geoacoustic uncertainties are interpreted as spatial variability of the seabed.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".