A Population Density Based Location Category System for Onshore Natural Gas Pipelines
Bibliographic record
Abstract
The location class system used in current North American pipeline standards (ASME B31.8 and CSA Z662) is based on structure count included in a specified assessment area. Because the number of people occupying different structures can vary significantly, the population density can also vary significantly for the same location class. Given that the risk (in terms of human safety) imposed by onshore natural gas pipelines is directly proportional to the population density, the current location class system leads to a large variation in the risk level for pipelines with the same class. To achieve more risk consistent designs, a new location category system is proposed in this paper using actual population density data collected from over 19,000 km of gas pipelines in North America. The boundaries between different categories in the proposed system are directly based on population density rather than structure count. One of the key features of the new system is that it uses a separate category for pipelines in unpopulated areas, which are a significant majority of the pipelines included in the study. The implications of the new system are discussed by comparing the lengths of pipelines falling into each category with the lengths of pipelines falling into each location class for all the pipeline data analyzed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".