Quantitative Risk Analysis on Rail Transportation of Hazardous Materials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The hazardous nature of the chemical materials is of significant concern in the economic viability of rail transportation globally. The potential risks of these materials to cause severe health impairments and catastrophic accidents have been widely studied and reported. Moreover, several models have been employed for assessing the risks associated with transporting hazardous materials by rail. However, a more holistic, quantitative, and robust model should incorporate more potential risk-triggered criteria, especially those causing severe health loss and devastating consequences like vapor cloud explosion. This study develops a risk assessment model by incorporating potential health risk factors and the obstacle circumstances. The potential risk factors are population density, route distance from residential areas, and the availability of sensitive third parties for health consequences. The proposed model utilizes Bayesian networks for causality modeling of the material release scenarios and fuzzy set theory for estimating the health effects and severity impact coefficient. Finally, individual risk curves and safe distances from the railway are developed. A real rail system for gasoline transportation in Tehran is investigated to evaluate the model’s effectiveness. The study provides panoramic leverages for risk-managed decision-making for safely transporting hazardous material by rail.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it