Bibliographic record
Abstract
Design of vertical alignment is one of the main tasks in highway geometric design. This task requires, among other things, that the designer ensure drivers always have a clear view of the road so they can stop before hitting an unexpected object in the road. Therefore, the ability to determine the required and available stopping sight distance (SSD) at any point of the vertical alignment is essential for the design process. Current design guides in the United States and Canada provide simple analytical models for determining the minimum length of a vertical curve that would satisfy the sight distance requirement. However, these models ignore the effect of grade on the required SSD. Alternative approaches and models have also been suggested but cover only special cases of vertical curves. Two specific models were expanded to determine the required SSD on crest and sag vertical curves. By comparing profiles of available SSD and required SSD on examples of vertical curves, it was shown that current North American design practices might yield segments of the vertical curve where the driver’s view is constrained to a distance shorter than the required SSD. An alternative design procedure based on the models was developed and used to determine the minimum lengths of crest and sag vertical curves. Depending on the approach grade, these new values of minimum curve length might be greater than or less than values obtained through conventional design procedures. Design aids were therefore provided in tabular form for designers’ easy and quick use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".