Impact of Soil Properties on Pipe Corrosion: Re-Examination of Traditional Conventions
Bibliographic record
Abstract
Soil corrosivity is not a directly measurable parameter and pipe corrosion is largely a random phenomenon. The literature is replete with methods and systems that attempts to predict soil corrosivity and resulting metallic pipe corrosion from soil properties (e.g., resistivity, pH, redox potential and others) surrounding the pipe. This paper describes research that endeavors to gain a thorough understanding of the geometry of external corrosion pits and the factors (e.g., soil properties, appurtenances, service connections, etc.) that influence this geometry. This understanding would lead to the ultimate objective of achieving a better ability to assess the remaining life of ductile iron pipes for a given set of circumstances. Varying lengths of ductile iron pipes were exhumed by several North American and Australian water utilities. The exhumed pipes were cut into sections, sandblasted and tagged. Soil samples extracted along the exhumed pipe were also provided. Pipe segments were scanned, using a specially developed laser scanner. Scanned data were processed using specially developed software. Statistical analyses were performed on three geometrical attributes, namely pit depth, pit area and pit volume. Various soil characteristics were investigated for their impact on the geometric properties of the corrosion pits. Preliminary findings indicate that the data not always support traditional conventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".