Bibliographic record
Abstract
Abstract The hull form and propulsion are essential for a successful ship. During the first 70 years of last century vessels for ice operation were developed mainly with trial and error method. Also when one design was found useful, it was duplicated to the next generation of ships. It was only in the mid 1950ies as the first model basing for developing ships for ice operation in Leningrad, USSR, started to take steps towards more scientific approach. In the late 1960ies after oil had been found in Alaska and the 100000 DWT tanker SS Manhattan had done its first voyage in the Canadian Arctic in 1969, the question rose, whether this could be modeled in smaller scale. SS Manhattan was the actual initialization for developing ice model tests to become a useful tool in modern shipbuilding. Since 1969 the testing methods and practices have changed and developed together with feedback from full-scale ship performance tests to be rather reliable tool to help the decision making process when we are talking about projects worth tens and hundred million dollars. This paper talks about different aspects of icebreaking and steps taken to develop vessels using ice model tests. Both hull form and propulsion development are discussed.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".