Investigating the difference in factors influencing the injury severity between daytime and nighttime speeding-related crashes
Bibliographic record
Abstract
This study investigates the differences in the factors affecting the injury severity of speeding-related crashes occurring in the daytime and nighttime. Two log-likelihood ratio tests are conducted to validate whether speeding-related crashes classified by daytime and nighttime should be modeled separately. The result proves that separate modeling is necessary. Two correlated random parameter order probit models with heterogeneity in means are conducted using the data collected from 2018 to 2020 in the United States. Model estimation results show that urban areas, speed limits, and young and older drivers are temporal instability. Angle crashes, head-on crashes, intersections, downhill, exceeding the speed limit, drunk driving, and motorcycles are statistically significant in both models with an increased crash severity. Interaction and heterogeneity effects between random parameters are also reported. For instance, large trucks driving above the speed limit are more likely to increase the probability of severe injury.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".