Experimental Investigation for Enhancement of the Safety of Transportation of Dangerous Goods
Bibliographic record
Abstract
The quantity and movement of Dangerous Goods (DG) depend on the economy of a country and its trading partners. One would expect that the better the economy the higher the volume of DG transported among other goods. In a developed country like Canada with its strong economy, large amounts of DG can be found on the various components of the transportation infrastructure. Ground transport moves approximately 21 to 31 percent of the total tonnage of DG in Canada. Accidents involving DG can occur at any time, at any location along transport routes or within storage areas, and they would not only affect people and the environment but would also have a great impact on the national economy. This paper presents the details of an experimental investigation studying the blast attenuation concept of four suppressive shield panels (SSP). The SSP technology can be used for storage, processing and transport of explosive materials and can also be applied to protecting attractive targets that is deemed vulnerable to explosive attacks. Various configurations of commercially-available steel angles were assembled as SSPs and were evaluated for their capability to attenuate blast pressure from detonating Pentolite charges. Results obtained from the experimental tests indicate blast pressure attenuation between 43 to 60 percent. This research can be extended to include the design and construction of SSPs for transportation of DG by sea as well by effectively strengthening the commonly-used transport 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 | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".