InfraGuide - Canadian national guide to sustainable municipal infrastructure
Bibliographic record
Abstract
The National Guide to Sustainable Municipal Infrastructure: Innovations and Best Practices (InfraGuide) is a most exciting municipal infrastructure project being developed by the Federation of Canadian Municipalities (FCM) and the National Research Council (NRC) under the largesse of the Government of Canada. The project's goal is to develop and disseminate best practices for Decision Making and Investment Planning, Roads and Sidewalks, Storm and Wastewater, Potable Water and Environmental Protocols. InfraGuide is also the focal point for a pan-Canadian network of practitioners, researchers and municipal governments focused on infrastructure operations and maintenance. InfraGuide represents the needs of municipalities across Canada. The concept has been developed through extensive consultations with a vast network of public and private stakeholders who continue to be involved in InfraGuide's development. The project is a direct result of requests made by Canadian municipalities for a renewed federal role in the development of inspiring methods for the optimization of municipal infrastructure management. A broadly based board of public and private representatives sets up the organization and the various development committees.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.029 |
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".