Sampling and Condition Assessment of Ductile Iron Pipes
Bibliographic record
Abstract
About 300 ft (91.4 m) of DI pipes were exhumed in each of four North American water utilities in an effort to gain a thorough understanding of the geometry of external corrosion pits in ductile iron (DI) pipes, which would lead to a better ability to assess the remaining life of these pipes. The exhumed pipes were cut into sections, sandblasted and tagged. Soil samples extracted along the exhumed pipe were also obtained. Pipe sections were scanned, using a laser scanner that was specially developed at the National Research Council of Canada (NRC)for this purpose and the scanned data were processed using special software developed for this purpose. The pipes were virtually sliced into rings of equal lengths, where each ring was characterized by three geometrical attributes, namely maximum pit depth, pit area and pit volume. Statistical analyses were performed on the geometrical attributes of the corrosion pits found on these rings. Soil characteristics were investigated for their impact on the geometric properties of the corrosion pits and were found not to have a substantial and consistent impact. Based on the results of the statistical investigation, methods were proposed to discern the condition of a ductile iron pipe based on a set of random samples. In this paper we describe the development of these methods, including the sampling scheme, the probabilistic inference on the pipe condition and the confidence bounds for the discerned results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".