Bibliographic record
Abstract
The energy transportation network of the United States consists of over 2.5 million miles of pipelines operated by approximately 3,000 companies. Based on data generated from annual reports to PHMSA from pipeline operators, the network includes approximately: • 173,000 miles of hazardous liquid pipeline; • 324,000 miles of gas transmission and gathering pipelines; • 2,037,000 miles of natural gas distribution mains and service pipelines; • 113 LNG plants connected to natural gas systems. There are 580,000 kilometers of pipeline in Canada, transferring oil and natural gas to various locations within the country, North America, and to ports, where products can then be shipped globally. As organizations change and grow, there is a need to determine not only the risk of a specific project or new asset, but the effect of that project or new asset on the risk profile of the facility or pipeline. Different types of risk evaluations may be performed at a location, so obtaining a risk score that can integrate various risk assessment techniques can be a challenge. This paper proposes a new technique developed to meet this need, called the cumulative risk assessment process. The cumulative risk assessment provides a quantified value for the operating risk at a facility based on the following formulae: BaselineRiskValue=L×C(1)OperatingRiskValue=BaselineRiskValue×(1×10−MitCredits)(2)CumulativeRiskValue=Σ(OperatingRiskValue)scen(3) Baseline risk is defined as the risk value in the absence of mitigation or risk control. Operating risk is the current risk level with existing mitigation and risk controls in place, evaluated in the calculation as mitigation credits. For the baseline risk calculation (L) refers to Likelihood and (C) refers to Consequence. Both baseline and operating risk are evaluated per scenario, with all scenarios summed to obtain the cumulative risk value for a location, pipeline or pipeline segment. This paper describes the cumulative risk assessment process and provides examples of how this risk assessment technique can be applied to an existing facility with new assets constructed and to a segment of operating mainline pipe.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".