Maximizing Your Investment: An Incremental Approach to Assessment of Critical Large Diameter Transmission Mains
Bibliographic record
Abstract
When New Jersey American Water (NJAW) experienced three failures on one of their major transmission mains, they faced the challenge of how to effectively and efficiently spend their budget to maximize the remaining life one of the backbones to their conveyance system. Recognizing that while the pipeline had previously failed, it may still have significant remaining useful life, NJAW embarked on a phased, incremental approach to assess the condition of the pipeline and ultimately make necessary improvements to the main, while maximizing the value of their investment. One of the most significant challenges was to determine if the pipe had a few isolated problem locations or if uniform deterioration existed, which caused the three failures. The flexible approach was structured to optimize the collection of pertinent data by using collection methods of increasing levels of resolution, such that once an adequate level of information had been collected, the process could be halted, and necessary repairs and improvements could be made. The intent was to develop a proven, repeatable process that could be used to assess all of their high-risk transmission mains. This paper will discuss the phased approach used by NJAW to confirm reliability of this pipeline for years to come.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".