First Experience in the Next Generation Ice Laboratory for Testing Ships and Structures
Bibliographic record
Abstract
Ice model testing has a history of almost 50 years. The first basin started operation in the middle of 1950ies in Russia by Arctic and Antarctic Research Institute (AARI). Ever since there has been a number of facilities built worldwide. In Finland the first facility was built by Wa¨rtsila¨ in 1969 for testing tankers intended for North-west passage (Manhattan project). In the eighties new facilities were built in Finland, Germany, Canada, Russia and Japan. In the present facility of Kvaerner Masa-Yards Arctic Technology (MARC) in Helsinki the operation started in 1983 under the name of Wa¨rtsila¨ Arctic Research Centre (WARC). The operation of the facility was originally planned to continue till 2011, but as part of the Helsinki City planning activity it was agreed that the facility is to end its successful work during 2005. In spring 2004 decisions were made by the new parent Aker Yards group and Aker Finnyards (that time Kvaerner Masa-Yards) to build a new facility and establish a separate company to handle ICE ISSUES for the whole Aker group. The new company, Aker Arctic Technology “AARC”, started operation in the beginning of 2005 and the new model testing facility was opened in February 2006. Aker Arctic Technology Inc. is owned by Aker Finnyards, Aker Kvaerner, Wa¨rtsila¨ and ABB. The services of the new company, in addition to the traditional model testing and related issues (environment studies, design bases and ship design concepts) will cover also total vessel design packages. This paper describes the novelties of the new ice model testing facility and reveals technical improvements, lessons learned and possibilities for more enhanced operation. Also the first experience in the new facility will be discussed.
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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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".