102 Avenue - Multi-Modal Safety Analysis, Best Practices, and Results
Bibliographic record
Abstract
The City of Edmonton is Alberta’s vibrant Capital City, and is one of Canada’s largest municipalities, with over 800,000 residents. The City’s population is expected to surpass one million residents over the next thirty years, which will place increasing pressures on its transportation system. The City recognizes the need to balance the needs of all road users – including drivers, pedestrians, bicycle users, and transit users. Light Rail Transit (LRT) expansion is one of the City of Edmonton’s top priorities for new infrastructure investment. In 2009, the City adopted a long-term LRT Network Plan that defines the future size, scale and operation of Edmonton’s LRT system. Construction has now started on a new LRT route from Downtown to Southeast Edmonton - the Valley Line LRT. By 2020, this new low-floor LRT line will be complete, including a section through Edmonton's Downtown Core along 102 Avenue. The LRT alignment through Downtown Edmonton will run along 102 Avenue. 102 Avenue is an important east-west corridor and serves an important multi-modal function for LRT, cyclists, and pedestrians. Recognizing the important multi-modal role of 102 Avenue, Edmonton City Council has approved a Downtown LRT Concept Plan for the corridor. City Council has also recently approved funding for the implementation of a physically separated cycle track on 102 Avenue prior to the completion of the Valley Line LRT. The purpose of this study was to review the previously approved plans and designs for the 102 Avenue corridor to identify safety and operational issues for all modes of transportation and to develop mitigation measures and alternative concepts to address the identified issues.
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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".