Quantitative assessment of escape and evacuation
Bibliographic record
Abstract
To enhance the safety inherent to a vessel's escape and evacuation system, attention has to be given at the design stage to several questions. Amongst these are the following: How long does it take to evacuate the vessel? What is the probability of a lifeboat colliding with the vessel after launch? When an evacuation involves multiple boats, such as a tightly packed line on a passenger ship, how can the launch sequence and timing be arranged to reduce the chances of having boats collide with each other in a rapid evacuation process? Each of these issues is influenced by factors such as the capability of the means of evacuation and how the capability deteriorates as weather conditions worsen. This paper describes some model scale evacuation experiments and presents results that can inform quantitative assessment of escape and evacuation procedures, including those above, which can be used to guide the implementation of risk control measures. The data presented are part of the Canadian contribution to the international FIRE EXIT project. The goal of FIRE EXIT is to develop design methods and software tools that incorporate the escape and evacuation knowledge derived from research projects, such as the one presented herein, that will make passenger vessels safe by design.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".