Reverberation models as an aid to interpret data and extract environmental information
Bibliographic record
Abstract
Reverberation measurements obtained with towed arrays are a valuable tool to extract information about the ocean environment. Preston pioneered the use of polar plots to display reverberation and superimpose the beam time series on bathymetry maps. As part of Rapid Environmental Assessment (REA) exercises Ellis and Preston [J. Marine Syst. 78, S359–S371, S372–S381] have used directional reverberation measurements to detect uncharted bottom features, and to extract environmental information using model-data comparisons. One enthusiast declared “This is like doing 100 simultaneous transmission loss runs and having the results available immediately.” Though that was clearly an exaggeration and the results are not precise, the approach provides valuable information to direct more accurate and detailed surveys. The early work used range-independent (flat bottom) models for the model-data comparisons, while current work includes a range-dependent model based on adiabatic normal modes. A model has been developed which calculates reverberation from range-dependent bottom bathymetry, echoes from targets and discrete clutter objects, then outputs beam time series directly comparable with measured ones. Recent work has identified interesting effects in sea bottom sand dunes in the TREX experiments. This paper will provide an overview of the earlier work, and examples from the recent TREX experiment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".