Methodologies for Evaluation of Corrosion Protection for Ductile Iron Pipe
Bibliographic record
Abstract
Repairing the nation’s infrastructure and ensuring its future durability are key issues facing the United States. It is important that these critical issues are addressed with appropriate and economical solutions based on the best modern scientific information. For example, in 2013 the American Society of Civil Engineers (ASCE), in its Report Card for America’s Infrastructure, gave the state of the critical water/wastewater infrastructure an overall grade of “D.” According to the ASCE, 6 billion gallons of drinking water disappear every day, mostly due to leaks in old pipes. Corrosion of water pipes plays a role in their durability and, for external pipe corrosion, the corrosivity of soils varies by location and is based on several factors, including soil resistivity. There is universal agreement that water pipes require some type of corrosion mitigation strategy (i.e., bare pipe is not installed). However, in specifying which strategy to employ for ductile iron pipe (DIP) in different soil types, the recommendations of the US Bureau of Reclamation (USBR) Technical Memorandum 8140-CC-2004-1 were subject to considerable debate. So much so that in 2009, this report was the subject of a study by the National Academy, which did not resolve the debate about the most effective and affordable solutions. Zinc-coatings have been explored in other parts of the world. Despite being widely used in Europe for almost 60 years, zinc coating was barely mentioned in either the 2004 or 2009 studies, which appeared to focus on North American data.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".