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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".