Jäissäkulkevan matkustaja-aluksen evakuoinnin turvallisuusarvio
Bibliographic record
Abstract
As there are few regulations for vessel evacuation systems in ice, a risk-based approach to defining requirements for safety in such circumstances is needed. In order for these requirements to be proactive in improving safety, a safety assessment is performed. The study reports the findings of a hazard identification workshop, where the goal was to identify hazards related to the subject matter and prioritize them based on the risk level. This information is used in generating a survey to gain better knowledge of the risks. A major part of safety assessment is the risk analysing step and for this purpose, the study uses a method called Bayesian modelling. The Bayesian network, a probabilistic graphical model, is based on the knowledge provided by experts in the form of survey answers. To gain extensive knowledge of the major circumstantial factors related to cold regions, the survey was completed first in Canada and then again in Finland. The risks of evacuation in the vast Canadian Arctic in comparison to the closed Baltic Sea are examined. The nature of safety analysis allows also investigation into the effects of risk control options (RCO). This enabies focus on the solutions that are found to be the most effective in diminishing the risks in the future. The study divides the evacuation process into the five most hazardous categories identified by experts: life rafts, lifeboats, training of the crew, remoteness and personal protective equipment (PPE). These categories form the core of the mathematical model. The study pinpoints the shortcomings in these areas and investigates means to enhance the safety during evacuation. The emphasis, however, is as much on creating a simple tool to be used for reviewing the risk level, as it is on actual results.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.021 |
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".