New Algorithm for Calculating 3D Available Sight Distance
Why this work is in the frame
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Bibliographic record
Abstract
The importance of considering three-dimensional (3D) sight distance in geometric design has been demonstrated by several researchers. However, little progress has been made in the use of 3D analysis in the design of highways. This can be mainly attributed to the complexity of the computations required for 3D analysis. This paper presents a new algorithm for calculating the 3D sight distance. The algorithm is considered more efficient, less computationally intensive, and more flexible than previously developed approaches. The algorithm is based on a parametric representation of the roadway and roadside features without any implicit approximation of the roadway surface. The paper presents analysis results of various alignment configurations to demonstrate the use of the algorithm and to examine the influence of different geometric elements on the 3D sight distance. The paper also provides an analytical tool that can investigate and determine the conditions under which a 3D analysis is considered necessary.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it