Bibliographic record
Abstract
Summary Different clearance methods in traffic accident management lead to varied duration distributions. Apart from investigating the influence of various factors associated with accidents on the duration of such accidents using different clearance methods, this study also examines the cumulative incidence probability. We used traffic accident data obtained for 12 months from the Fourth Ring Expressway main line in Beijing to develop a subdistribution hazard regression model, which can assess the risk factors of two clearance methods. The regression results show that the different factors have statistically significant effects on the duration of two accident groups with different clearance methods; furthermore, opposite effects occur even for some factors that have a strong effect on both accident groups. For example, an accident involving a taxi extends the duration time with clearance method 1; in comparison, the accident is shorter with clearance method 2. The predicted cumulative incidence curves of the two types of clearance methods are shown as examples, with stratification based on the influence factors (taxi involved, season). Finally, the Gray test of the cumulative incidence functions and the log‐rank test of the Kaplan–Meier estimates of the survival functions are compared, in order to demonstrate the importance of using proper methods for analyses. Copyright © 2014 John Wiley & Sons, Ltd.
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 | 0.027 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".