Meta-Risk as a Method for Addressing Uncertainty in a Pipeline Risk Management System
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
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.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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