Bibliographic record
Abstract
Shipping and offshore petroleum industry operations in Arctic and sub-Arctic regions have to account for an environment characterized by cold temperatures, remote locations, and a wide range of sea ice cover. To do so successfully, environmental factors must be addressed at the concept design stage. The environment affects operations on multiple levels: special structural design and steel grades to withstand ice loads under cold temperatures; robust propulsion systems to ensure reliability under propeller-ice interaction; winterization measures such as heating, insulation of fire mains and cooling water pipes, arrangement of access ways, icing, and extended low light conditions; and the human factors of working in a cold, remote, dark environment for extended periods. Design and operation in such environments requires special knowledge, skill and technology. This applies as well to the design and operation of the vessels' safety systems, including evacuation craft. An evacuation scenario must be executed in the ice conditions that prevail at the time of the emergency. In order to design an appropriately robust emergency response capability, it is essential to know what to expect of evacuation systems in terms of their utility in the presence of ice. This paper presents the results of an experimental campaign that investigated the performance capabilities of several lifeboats in ice. A series of model scale experiments was done in an ice tank to examine the effects of ice concentration, floe size and thickness on the lifeboats' abilities to launch and make way through the ice. Three different hull forms were tested to see how changes in shape might change performance. Likewise, changes in the delivered power were investigated in terms of simple performance benchmarks. Conclusions drawn from the model tests are presented and 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".