Bibliographic record
Abstract
This paper examines the impact of Mike’s research on geoacoustic inversion for characterization of sea bed sediments. First is his paper in 1987 that reported the use of vertical coherence of ambient ocean noise for estimating critical angles of sea bed sediment. Apart from the novel use of ambient noise as a sound source, his work opened the idea to make use of information about the ocean bottom contained in spatial phase relationships in vertical hydrophone array data. The practice was quickly adapted and widely applied as matched field inversion. However, the inversions were generally carried out using visco-elastic theory of sound propagation, an approach that is not the most appropriate for applications with porous sediment material. Mike’s next contribution addressed this issue in his series of papers starting around 1997 on the grain-shearing and viscous grain-shearing models of sound propagation in porous media. His theory provided a physical basis for the model parameters that are used to describe the interaction of sound with porous sediment media in geoacoustic inversions. Recent examples are shown that indicate how these innovations have become standard practice in geoacoustic inversions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".