Bibliographic record
Abstract
Subsequent to publication of the Highway Safety Manual (HSM) by the American Association of State Highway and Transportation Officials (AASHTO) in 2010, the Ministere des transports du Quebec (MTQ) launched a process to calibrate the accident prediction models proposed in the manual. The objective of this process is to enhance the accuracy of the models to better reflect the context in other jurisdictions. The initial focus of the process, undertaken by the Direction de la securite en transport in conjunction with the MTQ's territorial branches, was rural two-lane two-way roads (HSM Chapter 10). For this type of road, the HSM supplies prediction models for three types of intersections – unsignalized three-leg with stop control on minor-road approaches, unsignalized four-leg with stop control on minor-road approaches and signalized four-leg – as well as roadway segments. A sample of approximately 50 sites was established randomly for each type of site. The models were designed to take into account information on local conditions (e.g. geometry, traffic) as well as crash data compiled over a three-year period for the selected sites. During compilation, it was observed that the proportion of crashes involving animals was highly variable from one territorial branch to the next and even between sites within a single region. Following consultation with road safety experts at a number of territorial branches, it was agreed that the calibration factor should, for a variety of reasons, preferably be calculated excluding crashes involving animals. It was also interesting to note that in general, the calibration factors obtained did not vary sharply from the unit, indicating that accidents occur in relatively similar proportions to what is observed in the United States. Next, the proportions of various types of accidents and severities were calculated using data from the sampling of sites selected for the calibration process. These proportions are applied to the total number of accidents calculated using the prediction model to arrive at the number of accidents of each type or severity. Calibrating the accident prediction method from the HSM in this manner for the Quebec context will assist road safety experts in conducting safety analyses more comprehensively and accurately.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".