Improving the Safety of Transportation of Dangerous Goods
Bibliographic record
Abstract
The transportation of dangerous goods (DG) represents an important portion of the overall freight transport worldwide. Ground transport (excluding pipelines) moves approximately 21% to 31% of the total tonnage of DG in Canada. Accidents involving DG might occur at any time at any location along transport routes or within storage areas, and not only do they have an effect on people and the environment, but also they can have a great effect on the national economy. This paper presents the details of an experimental investigation studying the blast attenuation capability of suppressive shield panels (SSPs). Suppressive shield technology can be used for the storage, processing, and transport of explosive materials and can also be applied to protecting attractive targets and infrastructure deemed vulnerable to explosive attacks. Various configurations of commercially available steel angles were assembled as SSPs and evaluated for their ability to attenuate blast pressure from detonating Pentolite charges. Results obtained from the tests with 0.5-kg charges indicated that the SSPs attenuate the blast pressure to values in the range of 43% to 60%. The results of this research can be extended to include the design and construction of SSPs for transportation of DG by sea as well. Effectively, this can include the strengthening of current standard containers.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".