Benefits of using basic, imprecise or uncertain data for elaborating sewer inspection programmes
Bibliographic record
Abstract
One key goal of sewer inspection programmes is to target segments in the worst condition. Despite the development of deterioration models, the influence of available data on models’ predictive power has not been studied in depth yet. In this article, numerical experiments have been conducted to answer three main questions: (1) How can the data most probably available within a utility be used to define an effective inspection programme? (2) Can we use an auxiliary variable in order to compensate effects of missing data on inspection programmes? (3) Is it worth to accept a degree of uncertainty within data instead of not having them? In other words, is it preferable to have uncertainty instead of incompleteness within utility database? In order to respond to these questions, we considered an asset stock and then degraded the information by introducing uncertainty, imprecision and incompleteness within, to form a utility's database. The results show that significant improvement of inspection programmes could be achieved by using the most probably available data within utilities. We also show that using the notion of ‘district’ can provide efficient results when the most informative factor ‘age’ is not available. Finally, it is shown that having uncertain data is preferable to having incompleteness.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".