Autonomous Underwater Vehicle operations in the Arctic
Bibliographic record
Abstract
There is an increasing interest in Arctic and Antarctic studies, both from a scientific and from a commercial point of view. Due to the unpredictable climate conditions and the ice cover, as well as the difficulty conducting AUV operations with support ships due to the cost and scarcity of icebreakers and the presence of thick ice, AUVs are an ever-increasing option due to their ability to survey underneath the ice at long intervals. With the increasing use of AUVs in unsupervised under-ice operations, users are becoming more receptive to the idea of these types of AUV missions, and the capability to do this is advancing. International Submarine Engineering Ltd is the only subsea AUV developer with the Arctic experience behind it, specifically in operational hours under the ice. There is no doubt that the basis for ISE's successes lies in using a reliable, robust AUV and the underlying twenty years of work contributed to its development. For ISE, research deployments in the 1980's and 1990's and subsequent AUV deployments also provided background experience that was invaluable. From there, pulling off a successful under-ice deployment was essentially a matter of planning and testing. An example of the capability of current AUVs to operate unsupervised is the high Arctic field work by two Arctic Explorer Autonomous Underwater Vehicles (AUVs) built by ISE for Natural Resources Canada (NRCan). They were deployed in 2010 and 2011 to conduct under-ice bathymetric surveys in support of Canada's sovereignty claim under the United Nations Convention on the Law of the Sea (UNCLOS). These were the first long range AUV missions to have been undertaken at high latitude, and the first in which seabed survey data was successfully gathered over long distances working from both ice camps and icebreakers. As Autonomous Underwater Vehicles (AUVs) are now exploring more challenging terrain than ever, the need for an obstacle avoidance system has become apparent. Obstacle avoidance systems (OAS) in unmanned systems are far from new. However, adapting existing methodologies to AUVs presents a new set of challenges. Last year, International Submarine Engineering Ltd (ISE) tackled the task of adding an OAS to its line of Explorer AUVs. ISE's experience with obstacle avoidance strategies started in 1985 when the technology was added to ARCS, ISE's first AUV. ISE is the only company in the world with proven under the ice capability using AUVs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".