Glider-based passive bottom reflection-loss estimation: Proof of concept
Bibliographic record
Abstract
This study presents the proof of concept, based on data collected by a flying glider, for extending the capabilities of autonomous underwater vehicles to the passive measurement of the seabed reflection loss, an important contributor to the transmission loss in shallow water scenarios. During the GLISTEN 15 experiment, a Slocum glider was equipped with an advanced acoustic multi-channel payload coupled with a nose-mounted eight-element vertical line array of hydrophones. By processing the ambient-noise field generated by wind and breaking waves at the surface, recorded while the glider is quietly hovering over the seabed, the system provides an entirely passive, in situ measurement of the bottom power reflection coefficient as a function of frequency and grazing angle. The theoretical foundations and the algorithms for processing the data are summarized and applied for the first time to data collected by a linear array mounted on a flying glider. The results are compared to those obtained by arrays moored in the vicinity of the glider during the same experimental campaign. The possibility of recovering the layering structure of the bottom by passive fathometry is also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".