Development of Canadian Architecture for Intelligent Transportation Systems
Bibliographic record
Abstract
Under the guidance of a steering committee of public- and private-sector representatives from the Canadian transportation industry, the development of the Canadian Intelligent Transportation System (ITS) Architecture was initiated in August 1999. In general, the Canadian effort subsumes all of the U.S. National ITS Architecture work and extends and modifies it to provide new services and areas of coverage and to reflect differences between the nations and the existence of new and different stakeholders. Since there is much commonality between the technical definitions of the two architectures, it is important to understand the explicit relationships. The development of the Canadian ITS Architecture is examined, and the differences between the architectures of the United States and Canada are illustrated. The development included an extensive review of other relevant ITS architecture and standards initiatives. On the basis of the review and significant ITS stakeholder input, an initial draft ITS architecture framework was developed that defined the user services, user subservices, and market packages applicable to Canada. After a review by ITS stakeholders, the revised ITS architecture framework was used to develop definitions of both the physical and the logical architectures of the Canadian ITS Architecture. The follow-up support activities anticipated for the Canadian ITS Architecture are reviewed, and the potential for iterative development with the U.S. National ITS Architecture is examined.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".