Determination of Significant Risk Threshold of Upstream Pipelines
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 pipeline risk assessment has been part of the integrity management in the industry in today’s environment of increasing regulatory and public oversight. One of the widely used risk assessment method is the score index method. This method has been used for more than two decades and is widely accepted in the pipeline industry. The input data is relatively easy to acquire. The method provides details of mitigation options and relative risk values. However, this method does not provide the simple decision making process. In risk management, it is always the question to choose the most cost effective mitigation option to use limited resources. On the basis of score index risk assessment method, a method to correlate the probability of failure score with actual failure probability, and leak impact factor score with actual failure consequence in monetary units has been developed. This method applies the monetarily calibrated consequence factor to the probability of failure to obtain a normalized and calibrated risk in monetary unit. By comparing the cost of an estimated mitigation program, the decision can be made. Recent regulations in Canada require that risk assessment must have a method to determine the significant risk threshold. Even though some industrial standards have some recommended methods or benchmark data for failure probability, there is no published method to determine the threshold of high risk. Some pipeline companies have the in-house personnel to develop an advanced method to meet regulation requirement. However, many pipeline companies need to have a practical and economical method to determine the significant risk threshold to meet regulation requirement, and to effectively allocate resources. This paper develops a method to determine the significant risk threshold that can be used as a decision-making criterion in pipeline risk management. This process is practical for industrial application, especially for upstream companies where operators have limited resources for advanced risk assessment. A case study is presented using this method based on upstream pipelines.
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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 | 0.000 | 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) | 0.000 | 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