Bibliographic record
Abstract
Working together with local municipalities, and with other Community Groups, the County of Essex has spearheaded a project to improve conditions for pedestrians and cyclists in this rural Southwestern Ontario region, and to help more people recognize active transportation is a valid way to move from place to place. The County Wide Active Transportation Master Plan (CWATS) has developed a comprehensive Active Transportation (walking & cycling) master plan to guide the County and Local area municipalities in implementing a county wide network of cycling and pedestrian facilities over the next 20+ years. The network development process included an inventory of existing conditions, establishing candidate routes and recommending an overall Active Transportation (AT) Network and associated facility types. The network is proposed to be implemented in three phases: Short Term 1-5 years, Medium Term 6-10 years and Long Term 11-20 years (Attachment 1). The complete recommended Active Transportation network is viewed as a connected system with different facility types that are designed to be comfortable and convenient for both existing and future users. The success of CWATS is dependent on the initial and on-going support of County and Local Municipal Councils. The plan was unanimously adopted by all parties in the fall of 2012. This project was nominated for the TAC 2013 Sustainable Urban Transportation Award. For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".