Municipal infrastructure investment planning (MIIP)
Bibliographic record
Abstract
The objectives of the MIIP project are to identify, evaluate and develop tools, procedures, and practices that can help infrastructure managers make strategic and cost-effective planning and management decisions.The project is now in its final months and many of the project deliverables are already available to the general public. These include: a survey of municipal infrastructure assets in Canada, a primer on strategic asset management, a report on geographic information systems for municipalities, case studies of municipal asset management, an evaluation of sewer condition assessment protocols, a report on social costs of infrastructure rehabilitation, reviews of asset management software systems, a report on opportunities for research in asset management, an analysis of the state of Canadian sewers and their remaining service life, and a framework for municipal infrastructure management for Canadian municipalities.There is strong interest from the majority of the current MIIP members to continue with a Phase II of the project. Areas of interest for MIIP Phase II include: decision support tools for asset management, modelling the deterioration of buried utilities, and a generalized framework for municipal infrastructure management .The current MIIP contributors are the cities of Calgary, Edmonton, Hamilton, Ottawa, Prince George, and Regina, the regional municipalities of Durham, Halton, and Niagara, and the Department of National Defence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".