Types of Uncertainty and Their Impact on Target Risk or Reliability
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
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.
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.
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 | 0.000 | 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