Bibliographic record
Abstract
Transport Canada's Intelligent Transportation Systems (ITS) Deployment Plan funds Public-Private Partnerships for ITS deployment and integration projects in Canada. The ITS Deployment Plan has been an effective component of the Federal Government's ITS Strategic Plan to develop partnerships with and between ITS stakeholders in Canada to support its ITS industry. In March 2000, Transport Canada solicited proposals for ITS deployment projects in its first round of funding. Projects were selected for funding based on specific criteria which reflected Transport Canada's priorities for deployment. Applicants were encouraged to submit proposals and assemble consortium of multiple partners who would contribute their expertise and/or funds to the project. In the end, for every dollar the Federal Government contributed, it was able to lever an additional three dollars from other sources. To be eligible, proposals were expected to support three documents, including Canada's ITS Strategic Plan, Canada's ITS Architecture, and a provincial or regional strategic plan. In addition to the funding provided, the ITS Deployment Plan encouraged the development of partnerships between industry stakeholders. It helped lay the groundwork and set out the framework for ITS deployment and investment in Canada. In an era of limited public sector funding, this experience demonstrates a way in which small public investments and a coordinated, thoughtful approach can result in the attainment of public policy objectives.
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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".