Bayesian geoacoustic inversion for sediment profiles consisting of a general gradient over a layered substrate<b />
Bibliographic record
Abstract
This paper considers Bayesian geoacoustic inversion and uncertainty estimation for seabed profiles consisting of a smooth gradient of general form overlying a potentially-layered substrate. The profile gradient is parameterized as a linear combination of Bernstein-polynomial basis functions, with the polynomial order determined objectively by applying the deviance information criterion. In this way the form of the gradient is determined by the data, rather than by a subjective prior choice. The substrate below the gradient consists of uniform layers defined by an unknown number of (zero or more) interfaces, sampled probabilistically with trans-dimensional inversion. (Zero interfaces corresponds to a half-space with uniform parameter values equivalent to those at the base of the gradient.) The inversion approach is well suited to seabed environments consisting of an upper layer of soft, fine-grained sediments in which geoacoustic properties vary as a smooth function of depth overlying a harder, layered substrate, such as at the ONR Seabed Characterization Experiment at the New England mud patch. [Research funded by the Office of Naval Research, Ocean Acoustics Program.]
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".