The development of risk criteria for high severity low frequency events
Bibliographic record
Abstract
Abstract Quantitative risk assessments (QRAs) are used within the field of process safety to decide the allocation of resources and risk reduction investments. Typically risk assessments involve the evaluation of probabilistic measures that estimate the average expected value for the situation being considered across a range of potential outcomes. The resulting expected value is then used to determine if a situation represents an acceptable or unacceptable risk based on a threshold value allotted to the risk. This approach often gives guidance that is at odds with the thoughts and behaviors of some stakeholders as illustrated by the “but what if it does happen?” type of question. This inconsistency results from the inherent limitation associated with expected value approaches in that the methodology is based on whether or not a mean assessed risk represents an acceptable risk while overlooking the possibility that a single scenario could represent an intolerable event. This article looks at an adjustment to traditional QRAs so as to assess both the acceptability of risk and the tolerability of the associated consequences relative to risk criteria. These adjustments have been found to better represent stakeholder perceptions of risk, more closely relate risk tolerance to corporate values and resources, and to better justify the use of various risk transfer strategies. © 2008 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 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".