Analysis of passing sight distance using first-order reliability method
Bibliographic record
Abstract
Current passing sight distance requirements for two lane highways by the American Association of State Highway and Transportation Officials are based on field studies conducted between 1938 and 1941 which use deterministic values for its design variables such as passing sight distance, speed of the passing vehicle, speed differential between the passed and passing vehicle etc. This report presents three methods to analyze reliability and serves as an extension to the revised model presented by Yasser Hassan, Said Easa and A.O.Abd El Halim whose model sought to improve older models by equally considering both observed passing behaviours of drivers and passing maneuvers that are consistent with two lane highways. Analysis of passing sight distance using first order second moment reliability method, advanced first order second moment and the ellipsoid approach to measure the probability of failure of the passing sight distance design, rely solely on the mean and variance (moments) of each randomly distributed variable in contrast to methods that rely only on deterministic values. Results show the advanced first order second moment and the ellipsoid approach provided more accurate results than first order second moment method which in turn provide a greater safety margin with the later also proving to be a much more robust and efficient method of performing a reliability
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".