Développement d'une méthodologie de repérage des conduites d'aqueduc présentant des fuites
Bibliographic record
Abstract
The present article deals with the problem of leaks in drinking water distribution networks. Because of monetary losses related to those leaks, and of the current municipal context, it is important to reduce them to an acceptable level. Several techniques allow efficient elimination of leaks. However, when municipalities wish to set up a program for leak elimination on their networks, these techniques can turn out to be expensive. This research aims at developing a new technique which allows municipal managers to target the most problematic sectors or pipe reaches. Unlike the other techniques, this one is based on the study of pressure drops caused by leaks. This new leak detection approach presents a certain advantage by providing municipalities with a general idea on the leak problem which affects their networks as a whole. The proposed technique allows the identification of sectors or pipes affected with leak problems so that it is possible to identify more precisely which ones request a more extensive auscultation through other existing techniques. The costs related to the use of these techniques might be reduced as a result of an efficient preliminary diagnosis.Key words: pipe, detection, water, leak, infrastructure, tracking, pressure, network.[Journal translation]
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".