Bibliographic record
Abstract
The American Association of State Highway and Transportation Officials (AASHTO) provide 5 methods for distributing highway superelevation (e) and side friction (f). Method 1 (linear) is inferior to Method 5 (curvilinear). AASHTO Method 5 deals with speed variations, but its complex mathematical calculation affects design consistency. Safety margin is the difference between design and maximum limiting speed. This thesis describes distribution of superelevation (e) and side friction factor (f) based on the EAU and SAU methods using AASHTO and two different curves from the unsymmetrical curve; the equal parabolic arcs "EAU Curve" and a single arc unsymmetrical curve "SAU Curve". The thesis also describes e and f distributions based on the optimization model. The EAU and SAU methods and Parametric Cubic Optimization Model improve highway design consistency based on safety margins. Examples show the methods and optimization model are superior to AASHTO methods.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".