Real-Time Pack Ice Monitoring Systems - Identification of Hazardous Sea Ice Using Upward Looking Sonars for Tactical Support of Offshore Oil and Gas Projects
Bibliographic record
Abstract
Abstract There is an increasing requirement for real-time detection of sea ice hazards. These hazards include the identification of thick ice keels, large hummocky ice, fast moving ice, rapidly changing ice direction and multi-year ice. Such information is needed in real-time to support tactical applications for safe routing of ships in heavy sea ice concentrations. More recently, a need has emerged for tactical support of offshore oil and gas activities in ice infested waters of the Arctic Ocean and in marginal ice areas such as the Barents Sea, the Sea of Okhotsk, the Caspian Sea, Baffin Bay, the Labrador Sea and East Greenland waters. Reliable upward looking sonar (ULS) instruments, including the ASL Ice Profiler for ice keel measurements and the Acoustic Doppler Current Profiler for Ice Velocity measurements have been widely used in these areas for many years. These instruments, which record data internally, are operated from subsurface moorings that are deployed and recovered by ship during times of minimal sea ice coverage. Providing real-time measurements from the upward looking sonar measurements operating under heavy ice cover pose new technological challenges. The use of surface buoys to relay data from subsurface instruments to shore facilities or satellites is not possible due to the ice cover itself. A more feasible approach is to transmit the data from each instrument using underwater cables on the sea floor and which link the instruments on the subsurface moorings to a bottom mounted or floating structure. For a floating structure, the use of high performance acoustic modems may be required. Previous experience with real-time ULS ice measurement systems dates back to operational projects undertaken from 2002 to the present. More challenging requirements for real-time ULS ice measurement systems are being addressed in much deeper and more remote areas of the Arctic Ocean such as the Barents and Beaufort Seas. Conditions in these areas can vary from short episodes of hazardous ice to more prolonged and severe ice conditions. ULS ice systems may be deployed on a yearround basis or used episodically strategically just before hazardous ice episodes begin. The requirements for timely and accurate ice information demand high reliability in support of ship navigation, offshore oil and gas drilling and development applications. The real-time ULS ice measurement system must be capable of operating for multiple years without servicing in conjunction with other metocean sensors packages (e.g. ice radar, satellite, winds). Multiple ULS measurement arrays will be needed over operational areas spanning distances of many kilometers. For these Arctic Ocean applications, cabled ocean observatory technology and advanced underwater acoustic modems become key enabling technologies.
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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 | 0.001 | 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) | 0.002 | 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".