Major process accidents: Their characteristics, assessment, and management of the associated risks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Major process accidents continue to occur with the advancement of modern process systems. Major process accidents should not be viewed as Black Swan and can be predicted and prevented. This article investigates the characteristics of process accidents. Based on which, a method for the diagnosis and classification of accidents is proposed. The proposed tool is applied to the Bhopal accident and the swine flu event. The case studies verify the effectiveness and applicability of the proposed tool. To tackle major process accidents, conventional risk assessment, and management approaches are inapplicable without adaption. Enormous research work is needed to develop new generation of methods and tools that enable safer process systems and operations. Knowledge and technological gaps are identified in this perspective. © 2017 American Institute of Chemical Engineers Process Saf Prog 37: 268–275, 2018
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it