A Universal Framework for Implementation of an Assess and Fix Water Main Rehabilitation Program
Bibliographic record
Abstract
The assess-and-fix approach was developed as an economical alternative assessment and rehabilitation strategy to optimize short- and long-term investment into water mains with a lower failure consequence, typically, distribution mains ≤300 mm (12″). As presented in AWWA M77, it introduces the concept of utilizing electromagnetic tool condition assessment techniques in a contiguous program operation with cleaning and lining to tailor the rehabilitation technique selection to the specific needs of the main. While the technologies that support an assess and fix approach are a collection of mature, well understood technologies and the potential economic benefits can be profound (e.g., the same level of investment could increase annual rehabilitation coverage by 40%–50% on a length basis), widespread implementation progress has been impeded by a lack of understanding of the framework required to support program implementation from a logistical perspective.
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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 | 0.051 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".