MIIP Report: a case study of use and external components of social costs that are related to municipal infrastructure rehabilitation
Bibliographic record
Abstract
The Municipal Infrastructure Investment Planning (MIIP) project is a four-year collaborative project between the Institute for Research in Construction, six Canadian cities, three regional municipalities and the Department of National Defence. One of the project deliverables is research in the area of social costs. The main objective of this client report is to establish a general procedure to quantify user components of social costs related to municipal infrastructure rehabilitation and constructionprojects. User costs include travel delay costs, vehicle operating and maintenance costs, and cost of accidents. Existing user costs quantification models are identified and modified where necessary to represent Canadian urban environment.The proposed methodology is applied to actual infrastructure rehabilitation and construction projects carried out in the City of Regina, Saskatchewan, Canada in summer 2006. It is identified that travel delay costs represent a major part of project's user costs. Effective mitigation strategies are proposed as a result of this research. These include scheduling work for off-peak hours such as evenings and weekends; clear and accurate marking of work zone and detours; coordinating with other work in close proximity; detours through industrial instead of residential areas.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".