Quantifying data information content to resolve seabed structure in geoacoustic inversion
Bibliographic record
Abstract
This paper considers the importance of quantitative model selection and general parameterizations in estimating and interpreting seabed profiles in geoacoustic inversion, with application to data collected on the New England Mud Patch. In particular, the seabed structure that can be resolved depends on the information content of the acoustic data set under consideration, which varies with a number of factors, including the physics of the seabed acoustic interaction, frequency content of the data, and measurement and theory errors. Quantitative model selection applied to general parameterizations ensures the inclusion of seabed structure that is reliably sensed by the data while avoiding spurious structure. Data sets considered here include ship noise, modal dispersion, and wide-angle reflection coefficients. In each case, seabed models consistent with the data information content are estimated as part of the inversion using trans-dimensional and/or Bernstein-polynomial parameterizations. Results for all data sets indicate a low sound-speed mud layer over higher-speed sand; however, the ability to resolve structure within the mud layer, such as a transition to higher speeds near the mud base and possibly a weak positive gradient in the upper mud, depends on the information content of the various data sets. [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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".