Development of hazard-specific truck crash modification factors for cold-region rural highways
Bibliographic record
Abstract
This study attempts to develop (i) truck safety performance functions (SPFs), and (ii) hazard-specific crash modification factors (CMFs), for cold-region rural highways. Police-reported truck-involved crashes on rural highway segments of Alberta, Canada, were used to develop truck SPFs for four crash severity levels: total, fatal, personal injury (PI), and property damage only. Three settings of the Poisson–Tweedie regression modeling approach representing Poisson, geometric Poisson, negative binomial distributions were used to develop truck SPFs; the negative binomial distribution was deemed as the most appropriate distribution to model truck-involved crashes for all crash severity levels. The CMF for poor visibility (CMF = 1.5) suggests that poor visibility increases PI-type truck-involved crashes on rural two-lane two-way highway segments by 50% as compared to the number of such crashes attributed to crash causes other than transportation hazards. Road safety researchers may adopt the methodology to effectively rank hazard risks to highway freight transportation systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".