Application of interactive threat matrix induced system dynamics model to determine risk probability and resilient policy measures for CO2 pipelines
Bibliographic record
Abstract
In the sphere of decarbonization, a comprehensive CO 2 (Carbon dioxide) pipeline risk analysis framework is crucial for resilient long-term operations. Canadian Standards Association (CSA) updated regulations Z662:23 requires operators and regulatory bodies to develop quantitative risk assessment methodologies with probability and consequence analysis. Thus, this study is aimed at determining risk probability of CO 2 pipelines across Canada, while developing a simulation tool for consecutive policy analysis. The process involves integration of threat matrix from real gas pipeline incident dataset, long-short term memory (LSTM) model and system dynamics (SD) simulation. Baseline simulation represents a risk probability value of 5.89 with a synthetic integrity of 55.1 % by 2055. Sensitivity analysis, calibration, scenario analysis and structural validity have been performed to check the numerical boundary adequacy, accuracy and variability of the built SD model. Among two policies simulated, Policy 2 has been found to be more resilient, as it restrained the risk probability to a value of 2.54 with an increased 77.4 % pipeline integrity. The developed methodology is a simplified risk probability analysis tool for CO₂ pipelines, with extensible features to incorporate further consequences and economic analysis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".