Shallow-water reverberation highlights and bottom parameter extractions from the STRATAFORM
Bibliographic record
Abstract
Together with SACLANTCEN, the authors recently participated in the Boundary Characterization Experiment to measure shallow-water bottom reverberation in the STRATAFORM off New Jersey. SUS charges were used as monostatic sources. The receivers were horizontal arrays. Data were analyzed in bands from 160–1500 Hz. The STRATAFORM is known to have benign surface morphology but contains many buried river channels. Highlights of the reverberant returns are discussed that include returns from over the shelf break. Some comparisons in reverberation characteristics between SUS and coherent pulses are noted. Another objective of these reverberation experiments was to quickly invert for bottom scattering and bottom loss parameters. An automated geo-acoustic parameter extraction method was used together with the Generic Sonar Model and a Jackson-Mourad model for scattering. After automatically adjusting bottom loss and scattering strength, good agreement is achieved between the diffuse reverberation data and model predictions in relatively flat areas. Model/data differences are generally correlated with bottom scattering features. Since reverberation typically lasts 10–20 s or more, extracted parameters apply over wide areas. Local bottom loss and backscattering measurements were made by Holland in these areas. A comparison with Holland’s results is given. [Work supported by ONR Code 32, Grant No. N00014-97-1-1034.]
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".