Leak Detection on Wastewater Forcemains and Siphons in North America Using the Sahara® Acoustic System
Bibliographic record
Abstract
A series of pilot studies across North America have been held to demonstrate that the Sahara® leak detection system can be adapted to detect leaks in pressurized wastewater forcemains and siphons under typical North American operating conditions. Likewise, the Water Research Council (WRc) in the UK has demonstrated the applicability of the Sahara® leak detection system to similar environments in the UK. Sahara® wastewater is a new technology that allows utilities to assess the condition of critical wastewater forcemains and siphons, such as major non-redundant lines, waterway crossings, and lines through environmentally sensitive areas, while keeping the line in service; it is the first to allow full length inspections of in-service force mains. This paper discusses the need for inspections of wastewater forcemains and siphons, the Saraha® wastewater acoustic system, and the need for benchmarking of new technologies by third party organizations. The application of the Sahara system for the inspection of six wastewater pipelines in North America will also be discussed in detail.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".