Severity Analysis of Wildlife–Vehicle Crashes using Generalized Structural Equation Modeling
Bibliographic record
Abstract
Each year, thousands of wildlife–vehicle crashes (WVCs) occur in North America with negative effects on wildlife welfare, human health, and the economy. Although previous studies have investigated factors related to WVC frequency, limited research has been conducted on factors affecting WVC severity. Using more than 10,000 WVCs occurring in the province of Saskatchewan (Canada), this study investigated the severity outcomes of WVCs and their influencing factors, using structural equation modeling (SEM) with generalized (ordered probit) links. Compared with traditional severity analysis techniques, SEM offers the added advantage of representing, estimating, and testing complex modeling structures that include both measured and latent (unmeasured) variables. Three latent variables were introduced in this study: driver’s speeding attitude (SA), driver’s visibility impairment (VI), and crash severity. Measured variables obtained from crash records were included in the SEM to define latent constructs, and the resulting network of relationships was tested. The results showed that crash data supported the model hypothesis well, and the measured/latent variables adequately predicted crash severity. Overall, SA and VI were demonstrated to positively affect crash severity with SA being the most influential factor. Moreover, it was demonstrated that road surface condition was the most influential factor of the SA measurement model, and weather condition was the most influential factor with respect to VI. Finally, a comparison between generalized SEM results and traditional crash severity modeling using ordered probit links was conducted. Similarities and differences between these two approaches were discussed at the end of the study.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".