FINAL REPORT : A NATIONAL AGENDA FOR TECHNOLOGICAL RESEARCH AND DEVELOPMENT IN ROAD AND INTERMODAL TRANSPORTATION
Bibliographic record
Abstract
This report documents the conclusions of the TAC initiative to develop a National Agenda for Road and Intermodal Transportation in Canada. The agenda identifies trends, opportunities and needs, as well as specific high priority RD Asset management; Vehicle technology and environment; Transportation of goods; Pavement technology; Structures; Safety and human factors; Traffic management and ITS; Winter maintenance operations. Information presented for the nine technology sections is systematically organized and identifies technology-specific trends, opportunities and needs, as well as over 50 specific R&D projects recommended for implementation. The recommended R&D projects are described in terms of proposed solution, expected benefits, partnership opportunities, and resources required. The availability of the agenda is only the first step in advancing, invigorating and co-ordinating R&D activities. An action plan is needed to implement the agenda and we need to keep in mind that the proposed agenda needs to be periodically reviewed. This report is available online at www.tac-atc.ca/resource/resource.htm(A)
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 | 0.031 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.015 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".