Geoacoustical oceanography: Integrating geology with ocean acoustics.
Bibliographic record
Abstract
Knowledge of the structure and material properties of the ocean bottom is essential for modeling sound propagation in shallow and deep water environments. Significant progress has been made in the past decade in developing inversion methods for estimating geoacoustic profiles that are used to account for the interaction of sound with the ocean bottom in numerical calculations of the sound field in the water. These methods rely heavily on ground truth information about the material properties and structure of the sediment to define realistic prior estimates of the bottom. Ground truth is obtained by various different geological and geophysical techniques such as sediment grab samples and cores, and chirp sonar and high resolution seismic surveys. This paper uses examples from the Shallow Water ‘06 experiment to illustrate the interplay between geological and geophysical information and acoustic data in generating geoacoustic profiles that are effective for modeling sound propagation in shallow water. One example demonstrates the use of prior information in Bayesian matched field inversion, and another example shows the combined use of chirp sonar survey data and modal wavenumber estimation to generate a geoacoustic map of the region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".