MIIP Report: case studies on municipal infrastructure investment planning
Bibliographic record
Abstract
This report summarizes five case studies in the field of strategic asset management from a number of partners within the Municipal Infrastructure Investment Planning project. The objective of this report is to present examples of best practices in the field of investment planning from municipal infrastructure assets. The case studies presented herein demonstrate the development of asset management in the partner municipal organizations. The report presents summaries of more extensive reports and presentations by the contributors. The first case study, from the Region of Durham, deals with the development of a strategic asset management plan for the Region's pumping stations. The next study compares the preliminary results of the implementation of an integrated decision support system for infrastructure in the City of Hamilton and the Department of National Defence. The third and fourth case studies, from the Region of Halton, investigate the utilization of closed circuit television inspection for wastewater infrastructure and the use of facility condition assessments and audits for capital planning. The last case study in this report is from the City of Edmonton and illustrates the steps and challenges involved in implementing an asset accounting 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".