A Decision-Support System for Ice/Iceberg Surveillance, Advisory and Management Activities in Offshore Petroleum Operations
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Bibliographic record
Abstract
Abstract Seismic, coring, drilling, and supply operations in ice- and iceberg-infested zones require large quantities of environmental and operations related information which should be available quickly and conveniently to the operations and ice advisory personnel. Specialized software is available to support ice advisory work, generate ice charts, and ice and iceberg drift forecasts. Specialized instruments are available for ice hazard tracking and the safe deployment of these instruments by aircraft and UAVs. This paper describes a toolkit that is being developed to bring together currently available systems into one convenient package. This will assist a ship or rig's OIM and officers and ice advisory team members by providing clear displays of the current situations with respect to the location and movements of ice in the proximity of the operations site. The system will improve the response time of forecasts, efficiency of work and quality of decision-making by OIM and their support personnel. This toolkit will be a decision-support system, which will forecast ice, keep track of hazardous ice features, provide alarms for any and all environmental conditions that might threaten the operation, and assist in planning of new operations. The paper describes the need for such a system, best practices in Ice Observation and Advising, the key system design criteria; main system components, inputs and outputs; the stages of development, the modes of application that will achieve acceptance in the field; and finally, the benefits. Experiences of ice advisory projects undertaken in the Beaufort Sea, Baffin Bay, Sea of Okhosk, and the Caspian Sea are described. Rather than simply describe Ice Advisory work in the field, the paper casts current practice into the framework of standardized and best practices, and indicates how software, communications and instrumentation technological advances can be applied to support better decision making by expert teams that are distributed in space and involve people from different job functions and skill sets.
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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 it