PR-473-144506-R01 State of the Art Alternatives to Steel Pipelines
Bibliographic record
Abstract
This report is a literature review of several non-metallic material systems often used as alter-natives to steel pipelines. The pipeline systems reviewed are high density polyethylene (HDPE), fiberglass reinforced plastic (FRP), flexible composite and thermoplastic liners. This report is not intended to be a detailed guide or design manual on the use of the referenced materials for pipeline applications, rather an overall evaluation on the current state of these systems. Significant industry literature and documentation already exists on the design, manufacturing, installation, and operation of these pipelines. This information currently resides in pipe manufacturer's manuals and various industry standards and guides published by organizations such as ASTM International (ASTM), American Petroleum Institute (API) American Water Works Association (AWWA), and International Organization for Standardization (ISO). In Canada, the oil and gas industry pipeline code, CSA Z662-2015 (Canadian Standards Association, 2015). Users should frequently consult the manufacturers of the pipe products in use or under consideration for use for clarification and suggestions regarding the best practices, considerations and applications of the materials in question. In addition, pipeline operators should be aware of the applicable regulatory requirements in the jurisdictions they are operating within.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.033 |
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".