Bibliographic record
Abstract
This chapter presents the first case study on the city of Montreal’s municipal infrastructure, focusing on the roads, water, and sewer networks over a 9 km stretch divided into 20 corridors. The asset inventory includes details like corridor length, road width, traffic data, and pipe specifications, which are used in computational models for analysis. It discusses temporal, spatial, and financial datasets, highlighting the benefits of coordinated intervention scenarios over conventional ones, resulting in significant temporal and financial savings. It also analyzes the results of the optimization for both the PBC structuring and maintenance planning modes. The outcome of the PBC structuring optimization are KPIs’ thresholds and P/I while the outcome of the maintenance planning is an optimized intervention program; in other words, maintenance plan for both the conventional and full coordination scenarios. The results revealed an overall improvement of 15% in favor of the full coordination program compared to the conventional one. Those savings reflect a 10% improved reliability and less risk exposure, 18% reduced costs, 12% reduced disruption time, along with other savings detailed in the chapter. It concludes with sensitivity analysis to analyze the impacts of changing the reliability threshold on the other indicators. The results showed that the system is sensitive to changes in the reliability threshold. For instance, if the municipality increased their acceptable reliability threshold by 10%, it would potentially result in 42% more disruption time, 31% additional space, 33% extra cost, along with other impacts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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".