Comparison of range-dependent reverberation model predictions with array data from the 2013 Target and Reverberation Experiment
Bibliographic record
Abstract
Towed array data are very useful to explore the environment; data-model differences identify anomalies and uncertainties. Of particular interest here is the Clutter Model, which can be used to compare directly with measured towed array beam time series. Polar plots of the differences provide a bottom scattering map of the area, provided other anomalies such as volume reverberation from fish and other objects can be minimized. Here the Clutter Model predictions are compared with data from the 2013 Target and Reverberation Experiment from a nearshore area off Panama City, Florida, USA. Something interesting is happening near the shore, where the data show higher reverberation than the model predictions. An upslope test case identified a problem in the original Clutter Model implementation for strongly-range-dependent environments. This was corrected, and improved calculations made using several scattering functions. More work is required to determine if the remaining discrepancies are due to environmental effects, or if the adiabatic approximation of the normal mode formulation needs improvement. Overall the model predictions are in reasonably good agreement with the data, and improved from previous work. Backscatter with grazing angle dependence of sin3 or sin4 seems to provide a better fit to the data than sin2 (Lambert).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".