Analysis of safety-related incidents reported at a major North American petrochemical processing facility between 2016 and 2020
Bibliographic record
Abstract
Chemistry is a potentially dangerous enterprise. Major incidents in academic and industrial settings are commonly used in the form of case studies to suggest lessons and inspire change; however, a truism of safety studies is that major incidents often arise from the same causes as those that previously caused minor events that remained unaddressed. Major events make up only a very small percentage of the events that could, with multiple failures, lead to the adverse outcomes. In this study we examined 258 routine safety incidents reported between 2016 and 2020 at a major petrochemical processing facility site in North America. Restricting the analysis to incidents that included both chemicals and human error reduced the sample to 75 incidents, which were categorized via quantitative content analysis. Findings showed a significant effect of day of the week; most incidents involved mid-operation errors and related to administrative issues. Findings highlight a culture of reporting even superficially minor incidents, which may lead to improved safety implementation policies that could help prevent major incidents from occurring. We propose a crude measure to determine the rigor of a reporting culture: the incident severity reporting threshold index, the ratio between the number of incidents involving injuries and the total number of reported incidents. We strongly suggest that the severity threshold for reporting be lowered so as to address hazards and emergent risks before they have an opportunity to become dangerous.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".