Perspectives on Geoacoustic Inversion of Ocean Bottom Reflectivity Data
Bibliographic record
Abstract
This paper focuses on acoustic reflectivity of the ocean bottom, and describes inversion of reflection data from an experiment designed to study the physical properties and structure of the ocean bottom. The formalism of Bayesian inference is reviewed briefly to establish an understanding of the approach for inversion that is in widespread use. A Bayesian inversion of ocean bottom reflection coefficient versus angle data to estimate geoacoustic model parameters of young oceanic crust is presented. The data were obtained in an experiment to study the variation of sound speed in crustal basalt with age of the crust at deep water sites in the Pacific Ocean where the sediment deposits overlying the basalt are very thin. The inversion results show that sound speed of both compressional and shear waves is increasing with crustal age over the track of the experiment where age increased from 40 to 70 million years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".