Bibliographic record
Abstract
This paper provides the audience with information about traffic operation applications being used in a variety of countries such as The Netherlands, Germany, Switzerland, Sweden, Denmark, UK, Canada, Australia, France and several other countries. This information could be applied in the USA as well as in other parts of the world. Strategies to enhance pedestrian and bicycle mobility will be specifically addressed. Information about more rigorous approaches to the use of different roundabout designs to address the needs of specific types of intersections and road users will be included. The audience will be provided with CDs that will include a library of pictures that they can use to discuss application of the ideas presented in their own agencies. Many of the applications are oriented toward improving traffic safety, and the presentation will describe the techniques such as sophisticated detector placement at intersections to reduce the potential for broadside collisions. Video clips will be incorporated into the presentation to provide live demonstrations as to the effectiveness of some of the devices that will be discussed. Also included will be visualizations of traffic flow at intersections where different designs have been applied to illustrate how capacity has been improved without roadway widening. The information presented has been recently acquired and the audience will come away with tools that they can use in their own agencies to improve traffic operations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".