Real-time SAS processing for high-arctic AUV surveys
Bibliographic record
Abstract
Interferometrie Synthetic Aperture Sonar (InSAS) delivers ultra-high range independent image resolution with 3D seabed bathymetry at higher Area Coverage Rates (ACRs) and resolution than can be achieved with traditional physical aperture limited sidescan sonar. The along track resolution is achieved by synthesising the required aperture length by moving a physical aperture while sampling the field of view. The result is a compact power efficient solution ideally suited for use on Autonomous Underwater Vehicles (AUV) and towed platforms. This paper will discuss the advantages of SAS as a tool for search and survey applications, including a detailed analysis of the processing and data handling workflow. Example workflow steps include SAS processing (image and bathymetry formation), georeferencing, mosaicing, data formatting, map generation, data storage, data recovery and data visualization. In August 2014, Kraken Sonar Systems participated in the Victoria Strait Expedition to search for the missing ships from Sir John Franklin's doomed Arctic expedition. The expedition team used a variety of sonar survey tools, including a Kraken InSAS system, installed onboard the Arctic Explorer AUV owned by Defense Research and Development Canada (DRDC). Results from the search will be presented, as well as a discussion of the application of AUVs for high-arctic seabed survey.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".