Hazardous material transport accidents: analysis of the D.G.A.I.S. database
Bibliographic record
Abstract
In this paper results are presented and discussed about the analysis of the data of hazardous materials road transport accidents reported by the Canadian database D.G.A.I.S. (Dangerous Goods Accident Information System).The records of the database have been subdivided first of all on the basis of the transport phase to which they belong; then they have been classified by the accident typology and, for each type, by its primary cause.Subsequently accidents with hazmat release have been grouped considering the physical state of the spilled substance, in order to determine, basing on the leaked mass, the release categories and their occurrence probabilities.Finally the post-release event trees have been drawn and quantified for flammables.This work has allowed on one hand to determine the major causes of accidents and thus the actions to adopt for reducing the accident frequencies; on the other hand it has been possible to extract data for transport risk analysis, also suggesting useful improvements for bettering the quality of the information recorded by accident databases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".