Meta-Risk as a Method for Addressing Uncertainty in a Pipeline Risk Management System
Bibliographic record
Abstract
Typical risk assessment processes produce risk estimates by multiplying together single-valued, expected failure frequencies and associated consequences. However, a range of consequences can result from an incident, and a more representative estimate of failure frequency is captured by a distributed variable rather than by a single point value. Risk estimates calculated by typical assessment processes are sometimes referred to as “mean” estimates or “cautious best estimates”. This terminology acknowledges implicitly that there is truly a range of possible values. Meta-risk is a potential approach for analyzing risk that captures this uncertainty by utilizing distributions of failure frequency and consequence in place of point estimates. These distributions are combined to form a risk distribution that can then be used more directly in quantified decision making. Meta-risk improves on the principle of “As low as reasonably practicable” (ALARP) by acknowledging that the levels of uncertainty associated with models used in the risk assessment process are not equal. By providing “probability of exceedance” targets relative to defined risk acceptance criteria, the meta-risk approach allows for quantified decision making that addresses both the level of risk and the associated level of uncertainty. This process allows an analyst to compare risks more accurately from multiple hazards between which levels of uncertainty may vary greatly, and to quantify the benefits of integrity management strategies such as condition monitoring whose primary effect is to reduce uncertainty rather than to reduce risk directly.
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.
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 | 0.081 | 0.096 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".