Collision Frequency Analysis Using Tree-Based Stratification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although several factors are known to contribute to collisions at highway–railway grade crossings, the mixed effects of the control factors such as a highway class and other countermeasures on collision occurrence are less well explored. This study evaluates the relationship between countermeasures and collision occurrence by using a sequential analytic strategy that combines the tree-based data stratification method with the generalized linear regression technique. After the control factor effects were controlled with the use of the tree-based data partitioning method, stratified collision prediction models were developed, and the adjusted effect of the selected countermeasures was identified. The conventional statistical models were also provided for comparison with stratified models. The study used Canadian inventory and collision data to demonstrate the expected collision reductions resulting from changes in selected countermeasures, such as warning devices and posted speed limit.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it