A Deep Water, Under-Ice AUV: Extended Continental Shelf Mapping in the Arctic
Bibliographic record
Abstract
Abstract Arctic circulation drives multi-year sea ice against the Canadian Arctic Archepelago, making this margin one of the toughest regions in the Arctic Ocean to survey. Yet Canada had a need to map the seafloor in this region as part of its Extended Continental Shelf Program. One of the solutions to this challenge that Canada adopted was to develop an Autonomous Underwater vehicle that is mobile, could operate under the ice to 5000 m water depth, acquire bathymetric data and return to a location that is unknown prior to mission programming. A partnership program between Natural Resources Canada, Canadian Hydrographic Services, Defense Research Development Canada and International Submarine Engineering Inc.was launched to develop a vehicle that could be operated from an ice camp, work under ice, return to the drifting ice camp, and dump data and recharge while still in the water. The AUV was outfitted with a Knudsen 118 kHz single beam echosounder and a Kongsberg-Simrad EM2000 (200 kHz) multibeam sonar system. In 2010, the first trial of the under-ice AUV was undertaken. The system was launched near Borden Island of the Canadian Arctic Archipelago 400 km under the ice to be recovered at a drifting ice camp. It maintained a height of approximately 100 m above the seafloor and acquired single beam bathymetric data during its voyage. It was recharged at a remote camp and sent back to its base camp acquiring data on its return voyage. In 2011, the system was launched and recovered from an ice-breaker. It traveled 110 km under ice and acquired multibeam data along its track, travelling over difficult terrain during it's transect of a feature known as Sever Spur. The surface ship had drifted about 10 km from its deployment position during the mission, but the AUV was able to return within metres of the vessel.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".