Phase 1 of the framework for high quality data collection of urban goods movement in Canada
Bibliographic record
Abstract
This project was conducted to develop an understanding of the types of data that are needed to address urban goods movement issues as they relate to land use planning, infrastructure planning, traffic safety and operations, demand management and sustainable transportation. The research has provided a comprehensive overview of urban goods movement issues as they relate to infrastructure planning, land use planning, traffic safety and operations, demand management, and sustainable transportation. The research also identified the challenges facing practitioners as well as the best practices around the world. Phase 1 also found that there are many deficiencies with the existing data sets as well as gaps in data; and there is no single, comprehensive source of quality goods movement data for use in urban (or inter-urban) goods movement planning. Based upon this assessment, Phase 1 developed and tested a web-based questionnaire to identify stakeholders' current urban goods movement data collection practices, data usage and needs. A contact list of stakeholders was also developed. A planned future Phase 2 of the research will administer the survey across Canada in order to inventory existing urban goods movement data and to identify data needs.
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 | 0.061 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".