PLANNING FOR ITS RESEARCH & DEVELOPMENT: CANADIAN EXPERIENCE
Bibliographic record
Abstract
This paper describes recent Canadian experience planning research and development for intelligent transportation systems (ITS). The planning exercise was initiated by Transport Canada (the Canadian federal Department of Transport) and was facilitated by a cooperative agreement with the ITS Society of Canada (ITS Canada). The requirement for an ITS research and development (R&D) plan originated as one of the five pillars of An ITS Plan for Canada: En Route to Intelligent Mobility prepared by Transport Canada and released at the 6th World Congress on ITS in November 1999. The ITS Plan called for action on five pillars: partnerships for knowledge, an ITS architecture, a multimodal ITS R&D plan to foster innovation, ITS deployment and integration, and strengthening Canada's ITS industry. A discussion paper entitled Multimodal Intelligent Transportation Systems Research and Development Plan - Fostering Innovation was commissioned by Transport Canada to respond to the third pillar. The initial draft of this discussion paper was reviewed in a workshop designed to facilitate dialogue among all interested stakeholders. The present paper summarizes the conceptual model used to define the role of R&D in ITS and of ITS in transportation, the results of the workshop discussions and a preliminary list of application priorities for a multimodal ITS R&D program.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.016 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".