Transportation Association of Canada
Bibliographic record
Abstract
There is a growing demand for traveller information in Canada, and this demand has thus far been met primarily by the private sector (i.e. local broadcast media outlets). Canadian municipalities increasingly find themselves under pressure to provide more and better traveller information to the motoring, transit-riding, and walking public. This paper showcases the City of Toronto’s successful RoadInfo system and demonstrates the potential benefits and pitfalls of developing a telephone-based ATIS system. RoadInfo is a telephone-based traveller information system providing service to the public within the City of Toronto and across the Greater Toronto Area. With joint funding from the City of Toronto and the Ministry of Transportation for Ontario, RoadInfo has been providing planned event information, pedestrian operations information, comment capabilities, and limited live road closure information to area residents and businesses for over 10 years. The paper briefly reviews the benefits to be derived by ATIS systems, the development of RoadInfo system, the features RoadInfo provides, and its range of potential applications (e.g. from traveller information to public health advisories). The paper then identifies ATIS industry developments related to telephone-based services, and in
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.459 | 0.244 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".