Modélisation de l'évolution de l'état structural des réseaux d'égout : application à une municipalité du Québec
Bibliographic record
Abstract
Many sanitary and storm sewer networks are old and deteriorating rapidly. Water and sanitation managers are becoming more and more aware of the negative impact of poorly maintained sewer networks, especially since they are asked to comply with stricter environmental standards under already challenging budget constraints. The best way to improve the structural condition of a sewer network is to replace failed pipe sections. Planning replacement works necessitates knowledge of the present structural state of the network and of the evolution of this state in the near future. Towards this goal, a predictive modelling strategy was developed for the structural state of a sewer network. A case study in a Quebec municipality is presented to illustrate how the modelling strategy developed can be used to simulate the evolution, over the next 20 years, of the length of a sewer network in poor state, and to assess the impact of different replacement strategies on the global state of the network. Key words: urban infrastructures, sewers, structural state, modelling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".