Clustered Models for the Integration of Sewer Condition Classification Protocols
Bibliographic record
Abstract
Adoption of a suitable sewer pipeline condition classification protocol is recognized as an indispensable first step in world wide sewer rehabilitation industry. Various condition classification systems for sewers have been developed in this regard. These systems differ according to local requirements in which there is no integrated, unified sewer condition assessment protocol available. Therefore, in order to standardized sewer condition assessment procedures, there is an urgent need of developing such a system. This paper reviews the historical development of different sewer condition classification protocols. The protocols developed by the Water Research Centre (WRc), UK and by the Centre for Expertise and Research on Infrastructures in Urban Areas (CERIU), Canada, have been discussed in detail. In order to integrate both protocols, an unsupervised neural network model has been developed. The integrated protocols are verified by municipal practitioners and experts of the CERIU sub-committee for developing a unified sewer condition assessment system. The integrated models will assist municipal engineers in developing a unified sewer condition assessment system.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".