Use of towed array reverberation data for rapid environmental assessment
Bibliographic record
Abstract
Towed array reverberation beam time series can provide information about the underwater acoustic environment by providing a snapshot of the scattering in both range and bearing. By mapping time into range and beam angle into azimuth, John Preston pioneered the use of polar plots to survey an area [Preston et al., J. Acoust. Soc. Am., 87,119–134 (1991)]. When the polar plot is superimposed on the bathymetry of an area, a scattering map can be made. High scattering is generally associated with bottom features. With additional analysis and modeling, bottom loss and scattering strengths can be obtained [Preston and Ellis, J. Marine Systems 78, S359–S371 (2009)]. This talk emphasizes results from the rapid environmental assessment (REA) Rapid Response exercises 1996–1998, a multi-nation collaboration organized by NATO MILOC (military oceanography). While transmission loss experiments along a single radial could take hours, a single charge dropped near the towed array gave information on all radials in just minutes. Anomalies could be immediately identified, for investigation by more precise techniques. The REA exercises attracted interest and led to the Boundary Characterization and Clutter Joint Research Projects between CMRE (the NATO Centre for Maritime Research and Experimentation), Canada, and US. [Work supported by ONR.]
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".