Trans-dimensional reflection coefficient inversion of seabed sediments in two spatial dimensions
Bibliographic record
Abstract
We introduce an adaptive spatially two-dimensional (2-D) inference method for seabed sediment structure and geoacoustic parameters from wide-angle reflection coefficient spectra which does not require piece-wise one-dimensional (1-D) inversions or fixed assumptions about the 2-D parameter space. This is an advance in reflection coefficient inversion, more accurately capturing the information content of the data while retaining a parsimonious representation of the seabed. The approach employs (a) Bayesian inference with the reversible jump Markov chain Monte Carlo algorithm to allow the number of model parameters to change (i.e., trans-dimensional) and (b) 2-D Voronoi tessellations to enable a spatially irregular model grid with a variable number of cells. Synthetic tests indicate that the method estimates the 2-D geoacoustic model and its uncertainties in a more objective and straightforward way than approaches with fixed dimensions or multi-step 1-D inversions, while the computational cost remains similar to previous approaches. The resulting structure and uncertainties are more directly interpretable than those of fixed dimensional modeling methods.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".