Bibliographic record
Abstract
The Ontario Ministry of Transportation (MTO) released #CycleON: Ontario’s Cycling Strategy (Strategy) in 2013. The Strategy provides a 20 year vision to encourage the growth of cycling and improve the safety of people who cycle in Ontario. The Strategy establishes ambitions aspirational goals and commits MTO to develop a series of detailed Action Plans and associated performance metrics that will contribute to achieving the goals. A summary of the Strategy can be found in Appendix A and the complete Strategy can be viewed at http://www.mto.gov.on.ca/english/pubs/cycling-guide/pdfs/MTO-CycleON-EN.pdf. In 2014 MTO released #CycleON Action Plan 1.0 (Action Plan), the first multi-year plan for delivering the Strategy. The Action Plan, which is attached as Appendix B, includes 34 initiatives which are all underway and a couple that have already been delivered. While MTO is the #CycleON lead, it is working in partnership with other ministries, municipalities, cycling stakeholder organizations and the public. In fact, many of the commitments in the Action Plan are being delivered by other ministries and agencies of the provincial government. This program was nominated for the TAC 2015 Sustainable Urban Transportation Award.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.075 | 0.030 |
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".