A 30‐year review of copper pitting corrosion and pinhole leaks: Achievements and research gaps
Bibliographic record
Abstract
Abstract Despite decades of research, the pitting of copper pipes is poorly understood. This article summarizes the key research findings from 1990 to the present and identifies research gaps. Several lines of evidence suggest that, for soft waters, additional alkalinity or maintaining a pH <8.5 may reduce copper pitting. Phosphate corrosion inhibitors, used to prevent lead corrosion, may also moderate copper pitting in low‐alkalinity waters. However, such inhibitors could also exacerbate pitting due to insufficient coverage or could increase biological growth. Biological processes are likely involved in pitting corrosion, and practices to reduce biological growth may be beneficial. The research gaps identified include a need for better benchmarking, the effects of chemical transients (e.g., chlorine burns) and of hydraulic transients (e.g., water hammer), and changing the residual disinfectant from chlorine to chloramine. Article Impact Statement Approximately 750,000 pinhole leaks are estimated to occur in the United States each year, but we do not really know why.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".