Historical Analysis of the Types of Accidents in the North American Region
Bibliographic record
Abstract
Accidents can occur for several reasons in the chemical and petrochemical industries, whether during the transportation of hazardous materials or at industrial facilities. Frequently, the reasons for these accidents are physical effects that can damage the equipment. If equipment is damaged and a fire occurs, it can provoke a domino effect, increasing the severity of the primary event, as mentioned by Mercedes and colleagues (Gómez-Mares et al., 2008), where databases focused on European countries, such as the Major Hazard Incident Data Service (MHIDAS), are used to analyse the types of accidents that occurred due not only to fires, explosions, and gas emissions, but also different equipment malfunctions or the transportation of hazardous materials, with the total number of accidents being recorded. In the present work, an in-depth analysis of the different types of accidents that occurred in the chemical and petrochemical industries between the 90s and recent years in the North American region (i.e., the United States, Mexico, and Canada), has been carried out. The database used for the present research work was the International Disaster Database (EM-DAT Query Tool, 2020). On this platform, different types of accidents, such as those caused naturally, technologically, and by more complex disasters, can be investigated. In the present research article, a total of 340 accidents which occurred over the last 3 decades in the United States, Mexico, and Canada, have been analysed. The total number of accidents per decade for each country was collected and their types, causes, consequences, and number of deaths were analysed. Finally, safety measures have also been suggested through the comparisons obtained for the three countries.
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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 | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".