Performance of Ductile Iron Pipes. II: Sampling Scheme and Inferring the Pipe Condition
Bibliographic record
Abstract
Ductile iron (DI) pipes have been used in North America since the late 1950s. This paper describes how understanding gained on the geometry of external corrosion pits is used to devise a sampling scheme and to infer the condition of ductile iron buried water mains. The companion paper describes the exhumation of varying lengths of ductile iron pipes in four North American water utilities. The exhumed pipes were cut into sections, sandblasted, and tagged. Soil samples extracted along the exhumed pipe were also provided. Pipe sections were scanned for external corrosion using a laser scanner to produce corrosion pit data sets. Statistical analyses were performed on geometric properties of corrosion pits such as pit depth, pit area, and pit volume. These analyses were developed further to assess the impact of the different soil characteristics on these pit properties. This paper describes the investigation of appropriate sampling schemes to represent the statistical properties of ductile iron pipe corrosion. With known statistical properties, an approach is developed to infer the condition of the pipe.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".