Analyzing lateral seabed variability with Bayesian inference of seabed reflection data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper considers Bayesian inversion of seabed reflection-coefficient data for multi-layer geoacoustic models at several sites, with the goal of studying lateral variability of the seabed. Rigorous uncertainty estimation is carried out to resolve lateral variability of the sediments from inherent inversion uncertainties. The uncertainty analysis includes Bayesian model selection, comprehensive quantification of data error statistics, and a Markov-chain Monte Carlo approach to transforming data uncertainties to model uncertainties. 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, with posterior statistical validation. A Metropolis-Hastings sampling algorithm is used to compute posterior probability densities. Four experiment sites are considered along a track located on the Malta Plateau, Mediterranean Sea, and the inversion results are compared to cores taken at each site. Differences between profile marginal-probability distributions at adjacent sites are quantified using the Bhattacharyya coefficient. Differences that exceed the estimated geoacoustic uncertainties are interpreted as spatial variability of the seabed. The results are compared to an interpretation of geologic features evident in a chirp sub-bottom-profiler section.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it