Guidelines for Identification of Hazardous Highway Curves
Bibliographic record
Abstract
Curve-warning treatment can be extremely cost-effective because of the low cost of treatments, such as curve warning signs or markings, and the potentially large number of target crashes. However, measures are needed to identify and prioritize treatment of potentially hazardous rural curves. An empirical Bayes-based procedure is presented for prioritizing potential treatment sites on the basis of crashes that may be classified as occurring because of the presence of curves. The attractiveness of the procedure is enhanced by the fact that the data and calculations are also a part of the evaluation of treatment that may be applied to sites identified. Alternative levels of the procedure can be selected, depending on the data available. Ontario data were used to calibrate supporting models. It is hoped that experience gained from using the guidelines can lead to the development of procedures that can be incorporated into the U.S. Department of Transportation’s Manual on Uniform Traffic Control Devices.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".