To Separate or Not to Separate?: The Deerfoot Trail Case Study
Bibliographic record
Abstract
The purpose of this paper is to analyze the historical contrast between raised barriers and medians. While raised barriers prevent cross-median collisions, they often result in an increase in fixed-object crashes. This paper will discuss this paradigm and illustrate it using the example of the Deerfoot Trail in Calgary, where the occurrence of several high-profile fatal crashes raised the question of the need for raised median separation. This case study will review the characteristics of median involved collisions and determine its primary contributing factors. Not only will the need for a median barrier in the subject of the case study be analyzed, but a criterion for all cases will be presented. The application of various barrier systems and other median modifications to optimize median operations and safety will be presented along with a comparison of the various barrier systems based on installation cost, maintenance costs, deflection, impact forces and collision performance will be provided. Using the Deerfoot Trail as an example, the multiple considerations in the determination of an appropriate barrier system for a divided highway corridor will be demonstrated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".