Adoption of a Management System Approach to Geometric Design Process for Better and Safer Roads
Bibliographic record
Abstract
Transportation agencies are moving toward the development of enterprise-wide transportation infrastructure system (TIMS) or Transportation Asset Management System (TAMS). This is an opportune time to include geometric design and safety-based applications within the overall TIMS development. Alberta Transportation commenced the development of TIMS in 1996. As part of TIMS development, Network Expansion System Support (NESS) and Collision Information Application (CIA) were developed and implemented in 2007. NESS/CIA are geometric design and safety-based applications that are used for analysis in various phases of project development including capital planning, programming, planning, design, and rehabilitation phases. NESS/CIA performs highway network screening on roadway geometrics and roadway safety annually. Traditionally, geometric design and safety analysis are separate functions. Moreover, geometric design and safety applications are mainly considered at project level during the detailed design phase. The development of NESS/CIA applications enable geometry design and safety analysis to be assessed concurrently at various phases of project management. Over the past eight years, NESS/CIA demonstrated wide use applications in various phases of project development that would result in overall better and safer roads.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".