Canadian Guide for Greener Roads
Bibliographic record
Abstract
The Canadian Guide for Greener Roads (CGGR) promotes smart growth and multimodal transportation solutions along with safe, enduring roadway infrastructure and more sustainable construction and maintenance principles. The CGGR helps decision makers reduce the less desirable social, economic and environmental impacts of roads while encouraging processes, practices and products that will yield more sustainable outcomes. The CGGR is intended to help users self-evaluate and strengthen the benefits of integrating sustainability principles into Canadian roadway projects but it is not a design document. Users are encouraged to consult applicable jurisdictional requirements, technical standards and guidance documents, and should obtain professional advice as required. The CGGR is written for technical laypersons (e.g. environmental planner, transportation engineer) with some technical understanding but who may not be not subject-matter experts. The Guide does not address all aspects of sustainability, and only includes topics that were of most interest at the time when the Guide was developed. The document is accompanied with a set of sustainability practices available in a database. Using an interactive tool, readers may obtain information sheets on topics that can support achieving sustainability objectives for road infrastructure projects.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.137 | 0.063 |
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".