Cost‐Effective Application of Thermal Protection on LPG Road Transport Tanks for Risk Reduction Due to Hot BLEVE Incidents
Bibliographic record
Abstract
A simplified risk and cost-benefit analysis is presented for the application of thermal protection (TP) on propane and LPG highway tanker trucks operating in North America. A risk analysis is performed to determine the benefits of risk reduction by TP, relative to the costs of applying and maintaining TP on a tanker truck. The results show that TP is cost effective if the tanker truck spends enough time (or travels enough distance) in areas of moderate or high population density. The analysis is very sensitive to a number of inputs, including: (i) value of life, (ii) hot boiling liquid expanding vapor explosion frequency, (iii) public exposure to severe hazards, and (iv) life cost of TP. With this simplified analysis, it is possible to generate tanker truck exposure times to the public that justify the application of TP based on cost and benefit considerations.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".