Gestion des réseaux d'assainissement : évaluation et calage d'indicateurs de dysfonctionnement à partir de jugements d'experts
Bibliographic record
Abstract
Asset management is an increasing concern for wastewater utilities and companies. Indicators are developed for supporting the definition of investigation and rehabilitation programs. These indicators are mostly based on visual inspections, which provide major information. However, difficulty remains in the translation of a visual inspection survey into dysfunction indicators. Condition grade of a sewer segment may be obtained by comparison of a single score to thresholds which must be in accordance with practices or opinions of utilities' experts. The confrontation between expert assessments of sewer segments (condition grade) and calculated scores also demonstrates the necessity to consider diagnosis imperfection when establishing thresholds. To fill this niche, two calibration procedures are proposed in order to fix thresholds by minimizing a cost function: a crisp procedure and a fuzzy procedure. This article presents a comparison of both procedures on a case study. This approach is generally applicable to numerous domains, when levels of performance need to be defined / La gestion patrimoniale des réseaux d'assainissement est un enjeu de société dont l'importance ne cesse de croitre. La connaissance de l'état de santé global du patrimoine ou de l'état individuel de chaque tronçon est une donnée indispensable à toute stratégie de gestion. La question de l'évaluation de l'état d'un tronçon du réseau d'assainissement est donc un enjeu majeur et les inspections visuelles, et en particulier les inspections télévisées (ITV), constituent actuellement la méthode d'investigation privilégiée. Cet article explique comment interpréter ces inspections en vue d'évaluer l'état de santé d'un tronçon et surtout comment traduire une note sur le tronçon en état de santé. Nous présentons dans cette communication deux méthodes de calage destinées à définir les limites (seuils) entre chaque état : un calage « précis » et un calage « flou ». Ces méthodes de calage précis et flou sont comparées et discutées à partir d'un cas réel. Cette question du calage d'indicateurs est étudiée sur le cas des réseaux d'assainissement mais les résultats sont transposables à d'autres domaines.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".