Simultaneous consideration of the accident and terror risks for hazardous materials transportation
Bibliographic record
Abstract
We develop a methodology that incorporates the risk of terrorist attacks as well as the traditional accident risks. We consider the terrorist attacks against a single hazmat vehicle as well as a convoy including hazmat vehicles. Convoy formations, vehicle placements and movements, terrorists' attack capabilities are analysed in detail to estimate the undesirable consequences of an attack. A case study focusing on Turkey is developed. The proposed risk assessment method is incorporated in a GIS framework to identify the best paths among the common origin-destination pairs. Our findings indicate the necessity to update the traditional hazmat risk models in terror-prone areas, since there are significant changes in the optimal routes when the terror risk is also included. We suggest that hazmat vehicles should utilise wider, well-maintained roads in the event of a terrorist attack. It is advisable to integrate hazmat vehicles into convoys rather than allowing them to travel independently.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".