Bibliographic record
Abstract
Begun in 2006 as manual trial projects, the City of Calgary's lane reversals for Memorial Drive and the 5 Avenue Connector on Bow Trail have proven effective in reducing peak hour congestion and community shortcutting on key routes around the downtown core. Following the successful trial, the City moved forward with the Reversible Lane Control Systems (RLCS) project for planning, design, construction and commissioning of automated control systems for both reversals, making them a permanent feature of the weekday commute. The first such systems implemented in Calgary since the 1970s, the RLCS introduced a number of new control devices to the City's operations, including traffic gates and prism-type changeable message signs. Through an integrated fiber optic network and traffic cameras, the system is centrally controlled from the City's Traffic Management Centre (TMC,) providing a safe, effective and flexible system with which to transition the reversals on a daily basis. Commissioned in March 2010, the system makes effective use of under-utilized peak hour capacity to improve traffic flows in and out of downtown Calgary. Having maintained the footprints of the existing roadways, the project has integrated well with nearby, established neighbourhoods and is recognized as a success for sustainable transportation. The paper thus highlights a potential model for municipalities looking to find innovative ways to enhance transportation capacity on existing road facilities without undertaking costly or controversial road widening projects. (A) For the covering abstract of this conference see record control number 201111RT334E.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".