A Normal Mode Reverberation and Target Echo Model to Interpret Towed Array Data in the Target and Reverberation Experiments
Bibliographic record
Abstract
Reverberation measurements obtained with towed arrays are a valuable tool to extract information about the ocean environment. By superimposing a polar plot of reverberation beam time series on bathymetry maps, bottom features (often uncharted) can be located. As part of Rapid Environmental Assessment exercises, Preston and Ellis used directional reverberation measurements to extract environmental information using model-data comparisons. This early work used range-independent (flat bottom) ray-based models for the model-data comparisons, while current work includes range-dependent models based on adiabatic normal modes. Here, we discuss a range-dependent shallow-water reverberation model using adiabatic normal modes that has been developed to handle bottom scattering and clutter echoes in a range-dependent environment. Beam time series similar to those measured on a horizontal line array can be produced. Comparisons can then directly be made with data, features identified, and estimates of the scattering obtained. Of particular interest will be data obtained on the triplet line array during the 2013 Target and Reverberation EXperiments in the Gulf of Mexico off Panama City, FL, USA, where interesting effects in sea bottom sand dunes were observed. Particular attention has been paid to calibration to get estimates of scattering strengths. In addition to the reverberation, a preliminary investigation of the target echo is presented.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".