Managed Ice Loads on a Dynamically Positioned Vessel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Stationkeeping in ice-covered waters has become a large area of interest forresearch and development in light of heightened interest in Arctic oil and gasexploration. The performance of Dynamic Positioning (DP) control systems forstationkeeping purposes in ice conditions is a difficult challenge fornumerical modeling assessment. Given that full-scale validation data for DP inice operations is often scarce, physical modeling of stationkeeping in iceoffers the best method for assessing the performance of dynamically positionedvessels in these conditions. A series of model tests carried out at theNational Research Council of Canada's Ice Tank facility in August and Septemberof 2011 attempted to observe the effects of various managed ice conditions(i.e. ice floes which have been broken into manageable pieces by an icebreaker) on DP performance. Results from these tests are discussed. Ofparticular interest in this study is the observation of non-linear effects ofvarying ice conditions on DP performance. The use of machine vision-based dataproducts as potential estimators of ice loading is discussed. It is concludedthat simple statistical observations of these conditions will be unable tofully characterize the effects of various ice parameters on performance, andthat investigation into more advanced data products available from machinevision systems may be able to aide in characterizing these effects as well asin the development of models capable of predicting ice loads.
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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 | 0.001 |
| 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