Joint trans-dimensional inversion for seabed and water-column properties in the seabed characterization experiments
Bibliographic record
Abstract
This paper considers the information content to resolve both a geoacoustic model of the seabed and a model of the water-column sound-speed profile (SSP) in the inversion of modal-dispersion data. A Bayesian formulation of the inverse problem allows prior information representing varying levels of independent knowledge to be applied separately to the seabed and water-column models. Issues of interest include the extent to which knowledge (or lack thereof) of the SSP affects geoacoustic inversion, and the ability to remotely estimate the SSP from acoustic data in cases where the seabed is either well known or poorly known. The joint inversion for seabed and water-column properties is formulated in terms of a separate trans-dimensional model for each, providing an automated approach to quantitative model selection. The seabed model is formulated in terms of an unknown number of uniform sediment layers, while the SSP is formulated as an unknown number of depth/sound-speed nodes, with the reciprocal of sound speed squared varying linearly between nodes, as per normal-mode propagation models. The approach is applied to modal-dispersion data measured during the seabed characterization experiments at the New England Mud Patch. [Work supported by the Office of Naval Research]
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".