Chemical safety board investigation reports and the hierarchy of controls: Round 2
Bibliographic record
Abstract
Twenty‐five reports of the US Chemical Safety Board over the period December 2009–May 2016 were analyzed for evidence of examples related to inherent safety, passive, and active engineered safety, and procedural safety. These measures were also analyzed for their contribution to incident prevention and consequence mitigation, as well as their applicability to specific elements of the PSM system recommended by the Canadian Society for Chemical Engineering. This work represents the continuation of a previous study that performed a similar analysis for 60 CSB reports written during the period 1998–2010. The update provided here further illustrates the significant value of CSB investigation reports in demonstrating lessons learned and in training and educational efforts. Procedural safety was identified as the most common risk control measure cited in CSB reports, while the efficacy of inherent safety principles in incident prevention and mitigation has been consistently recognized by the CSB in its investigations. Risk reduction efforts aimed at incident prevention were found to be cited more often than those aimed at consequence mitigation. Active engineered safety measures were determined to be common among mitigation efforts due to the prevalence in the process industries of emergency alarms and fire suppression systems. © 2018 American Institute of Chemical Engineers Process Saf Prog 37: 459–466, 2018
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".