Liferaft performance in wind and waves: an experimental evaluation
Bibliographic record
Abstract
Liferafts are commonly used worldwide as primary or a secondary means of evacuation from passenger vessels, merchant ships, and offshore petroleum installations. In many cases, liferafts are required by regulations whose explicit aims are to provide for the safety of life at sea. Despite being almost universally prescribed and carried by ships and offshore platforms, the actual performance that can be expected of liferafts and the people who have to use them in practice is largely unknown. Current international standards for Life Saving Appliances require the successful completion of tests in calm water in order for manufacturers to obtain type approvals for their liferafts. However, the performance of liferafts in general ocean conditions is largely unknown. This absence of qualitative and quantitative knowledge of expected utility, especially in different weather conditions, weakens rational decision-making processes and a host of associated decisions in search and rescue operations and planning. The authors have been conducting research over the last three years on liferaft operational performance to address some of these unknowns. The research investigated liferaft operational performance in a range of weather conditions, at both model and full scale. This paper presents the results of tow and drift experiments on 16, 42 and 150 person rafts, and addresses a knowledge gap by providing empirical data that can be used by manufacturers, regulators and other professionals in their decisions concerning liferaft safety.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".