High Resolution Seabed Sub-Bottom Profiler for AUV
Bibliographic record
Abstract
Memorial University of Newfoundland (Memorial) is undertaking a novel and exciting area of interdisciplinary research and development related to Autonomous Underwater Vehicles (AUV). AUVs are an untethered, unmanned technology that enables a broad array of research, especially in hazardous underwater environments, that cannot be achieved by other means. In spring 2010, Memorial University commenced design work on a project that aims to provide a means to conduct high-resolution sub-bottom seabed surveys in water depths up to 1000 m (3281 ft), using a new imaging sub-bottom profiler technology with a 10 cm (3.9 in) resolution that has never been deployed on an AUV. The purpose of this project is to integrate a long-array sub-bottom profiler developed by PanGeo Subsea Inc. of Canada, into Memorial’s Explorer AUV by building a new vehicle section that resembles a thick airplane wing with a span of 3.5 m (11.5 ft). Memorial University is working to make the new equipment easily adaptable and removable from the Explorer AUV while in operation. The Explorer AUV equipped with this new sub-bottom profiler capability will be operational in 2012. In this paper, the underlying design criteria and challenges are discussed. A preliminary concept design is described and coarsely evaluated for technical feasibility.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".