Types of Uncertainty and Their Impact on Target Risk or Reliability
Bibliographic record
Abstract
The main objective in using reliability based methodologies is to provide consistent safety by explicitly accounting for uncertainties in a probabilistically quantified manner. Reliability methods also allow the articulation of the level of safety. This level of consistency in safety cannot be achieved in a deterministic analysis using safety factors. However, reliability based methods can be used to calibrate and improve deterministic methods to improve the consistency of the safety level. Providing consistent safety enables optimization of maintenance activities which enables the safest system to be provided using the available resources. Currently used deterministic and reliability based methods are both examined and discussed. Gaps and areas of improvement are identified with the objective of improving safety and explicitly articulating and communicating the level of safety. Effective use of quantitative risk and reliability methodologies requires quantitative data that describes the current state of the pipeline, the anticipated future state as well as the failure limit state. In maintaining oil and gas pipelines this level of quantitative data of the pipeline is available when pipelines are in-line inspected. Although reliability-based assessments are by no means restricted to corrosion management, the reliability based maintenance program at Pipeline Research Council International (PRCI) has been foremost applied to corrosion management because in-line inspection (ILI) data is adequately accurate to perform reliability based assessments. Guidelines for a reliability based maintenance program have been developed and projects executed to validate and demonstrate the implementation of these methodologies. The main learning from these guidelines and subsequent validation projects has been useful in identifying the process for improving integrity related decision making, the sensitivities of these methodologies, the impact of physical uncertainty and knowledge uncertainty, and the challenges in defining and applying target criteria. These identified areas are explored and discussed.
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".