The use of the Pareto shape parameter as a leading indicator of process safety performance
Bibliographic record
Abstract
Abstract Metrics addressing process safety incident performance typically focus on frequency and severity statistics. Often, these lagging metrics are not overly sensitive to actual performance, making trending and forecasting difficult. This article presents the results from a statistical study of a large incident dataset where changes in the Pareto shape parameter were observed as a function of time. This approach has been found to give far better insight into process safety performance than traditional incident metrics and readily relates back to concepts such as the “incident triangle” and “layers of protection.” Through the application of this approach, trends within process safety incident performance have been observed earlier, and more accurate forecasting has allowed for the identification of anomalies. In turn, these critical observations have allowed for the better structuring and targeting of process safety programs. Although incident data are generally considered as a lagging indicator, this approach has clearly reduced the lag time associated with this type of data and has given valuable insight into the current status of process safety performance. © 2009 American Institute of Chemical Engineers Process Saf Prog 2009
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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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".