A Canadian experience \342\200\223 InfraGuide
Bibliographic record
Abstract
The National Guide to Sustainable Municipal Infrastructure (InfraGuide) is a unique Canadian information resource for infrastructure project decision makers. It is funded mainly by Infrastructure Canada and managed jointly by Federation of Canadian Municipalities and National Research Council Canada. Other stakeholders augment the available funds for the project by their in-kind contributions through volunteers taking active parts in InfraGuide activities. InfraGuide has 5 Technical Committees currently each working on one of the following municipal infrastructure areas or activities; municipal roads and sidewalks, potable water, storm and wastewater, environmental protocols, and decision-making and investment planning. InfraGuide published to date over 50 Best Practice documents in total, covering these five domains. Published three documents pertaining to urban drainage issues cover most of the treatment train concept for urban drainage. The “Stormwater Management Planning” sets the stage for an integrated stormwater management planning approach, which considers stormwater as a resource to be protected. “Source and On-Site Controls for Municipal Drainage Systems” introduces the rationale of stormwater management programs. “Conveyance and End-of-Pipe Measures for Stormwater Control” sets its objectives as both prevention and mitigation of stormwater run-off quantity and quality impacts and explains a variety of methods and mechanisms to be applied.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".