Variability of target echo and clutter in the 2013 Target and Reverberation Experiment
Bibliographic record
Abstract
The Target and Reverberation Experiment was conducted in the Gulf of Mexico, off Panama City, Florida. Reverberation, noise, and target echo measurements were carried out for more than a month during April and May 2013, using a fixed source and fixed horizontal array receiver deployed in about 20 m of water. To support modeling efforts, a considerable number of ancillary environmental and transmission loss measurements were made. Various pulses in the 1.8–3.6 kHz frequency band were transmitted day and night, and have produced a rich data set. Initial results by various authors were published in the recent TREX13 Special Issue of the IEEE Journal of Oceanic Engineering. This work extends the analysis, concentrating on the variability of the echoes from various targets: a vertical hose, vertical arrays, a towed echo repeater, the hull of the towing ship, and other persistent bottom scatters, such as shipwrecks. In addition to the ping-to-ping variability of the echoes there are consistent trends, which are not understood, though probably due to oceanographic effects. An attempt will be made to relate the echo variability to measured and modeled reverberation and transmission loss. [Work supported by ONR, Ocean Acoustics, Code 22.]
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".