On the use of an autonomous underwater vehicle for seabed characterization.
Bibliographic record
Abstract
In 2009 NURC conducted the CLUTTER’09 experiment on the Malta Plateau, south of Sicily, in collaboration with the CLUTTER JRP partners from USA and Canada. One of the main objectives of this experiment was to characterize the seabed for geoacoustic and scattering properties related to clutter, i.e., around regions on the bottom which generate target-like returns on active sonar displays. The equipment used was a newly developed sound source and 32-m horizontal line array at NURC towed behind the Ocean Explorer Autonomous Underwater Vehicle (OEX-AUV). The source transmitted signals in the frequency band 800–3500 Hz and were received on the line array. The array has a four-level aperture which allows both for utilizing individual hydrophone data and beamformed data for the seabed characterization. The advantage of using the OEX-AUV is that measurements can be performed close to the seabed which are difficult to obtain from conventional sonar systems towed from a surface vessel. Results of environmental characterization using the OEX-AUV from a selected region are presented and compared to independent findings from previous experiments. [Work supported by the NATO Undersea Research Centre and the Office of Naval Research OA321]
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".