Modelling Reverberation in the Northern Gulf of Mexico
Bibliographic record
Abstract
Seafloor roughness is a major contributor to sound scattering and is thus an important component of seafloor reverberation models. Between April and May 2013 the TREX13 (Target and Reverberation Experiment 2013) sea trial was conducted in an area with a fine to medium grained sandy sea floor, just off the coast of Panama City, Florida. During this experiment numerous acoustic and oceanographic measurements were collected. Bathymetry and seafloor roughness spectra measurements collected during TREX have been analyzed and will be utilized as inputs for an acoustic scattering model. Results obtained from the scattering model will then be employed in a sea-bottom reverberation model, which will then be compared with reverberation measured during TREX13. Sub-bottom profiler data of seafloor acoustic reflectivity and FFCPT (Fee Fall Cone Penetration Testing) data collected during TREX will be used to help identify dominant scattering mechanisms. Based on results from the analysis of seafloor roughness spectra and bathymetry it is expected that the sediment will show uniform (isotropic) spectral characteristics. However, FFCPT and sub-bottom profiles indicate that discrete and volume scattering may also be observed.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".