Understanding human performance in ship evacuation
Bibliographic record
Abstract
In order to develop realistic and robust maritime evacuation procedures, it is vital to understand how passengers behave in emergency situations. An essential component of this understanding is the collection and characterization of human performance data. However, little data relating to passenger response time or fullscale validation data in maritime environments exists. Although the International Maritime Organization’s (IMO) evacuation protocol Maritime Safety Committee (MSC) Circ. 1033 and its successor, MSC Circ. 1238, are of great use, it is known in the industry that the existing data is not representative of passenger ships in general. \nThe SAFEGUARD project addresses the IMO Fire Protection Sub Committee’s requirement to collect full-scale data for calibration and validation of ship-based evacuation models, as well as proposing and investigating additional benchmark scenarios to be used in certification analysis. Funded through the European Commission’s 7th Framework Programme, the Newfoundland and Labrador Research and Development Corporation and Transport Canada (Marine Safety), SAFEGUARD has brought together leading industry experts and the project findings will play an integral role in framing the next iteration of international guidelines for ship evacuation analysis. \nThis essay describes the methodology undertaken within the full-scale assembly trials that were carried out – one of which included the largest ever real-life assembly trial on a passenger ship. The main findings are presented and highlight what this will mean for the future of ship evacuation.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".