Development, Evaluation, and Installation of a New Improved PVC (iPVC) Pipe for Water Applications
Bibliographic record
Abstract
Many U.S. utilities rely upon iron pipe to deliver water through their distribution network. It has only been in the past 50 years that other products have penetrated in the water utilities. The industry has turned to various cementitious and plastic materials largely to overcome aggressive soil and water that can be corrosive to the metallic pipes. This paper will discuss a current water research foundation (WaterRF) study on the development and testing of a new structurally enhanced polyvinyl chloride (PVC) pipe called iPVC. The pipe was developed in South Korea by Pyungwha Pipe Industry Inc. (PPI) and currently is used for water distribution systems in Japan and China as well as South Korea. The evaluation is achieved by series of selective testing on the pipe, including impact, stiffness, tensile, short-term hydraulic burst pressure, fatigue, and bedding test. The evaluations will also include pilot installation and operation of the pipe as part of an active system. This paper will discuss the results of testing and evaluation process including the field installation performance of the iPVC in the St. Louis County, Missouri by Missouri American Water. Prior to the evaluation, the iPVC pipe has passed the National Sanitation Foundation, NSF 61 certification and complied with the American Water Works Association (AWWA) C-900 standard.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".