Safety Comparison of New Jersey Jug Handle Intersections and Conventional Intersections
Bibliographic record
Abstract
New Jersey jug handle intersections (NJJI) have been around for the past few decades. The basic design philosophy behind implementing jug handle intersections at suitable locations is to improve traffic operations by the elimination of the left-turn phase on a major road and to improve traffic safety by a reduction of the total number of potential conflict points and specific conflicting maneuvers at the intersection. This study, based on statistical analyses of intersection crash data, investigates the differences between and similarities in safety performance of NJJIs and conventional intersections for a limited sample set of 44 NJJIs and 50 conventional intersections. Results from raw data indicated that conventional intersections tended to have more head-on, left-turn, fatal-plus-injury, and property-damage-only accidents and relatively fewer rear-end accidents than NJJIs. These observations were confirmed by negative binomial crash prediction models that were developed to account for the influence of other causal factors. Models were estimated for total, fatal-plus-injury accidents, rear-end, and sideswipe accidents for both sets of intersections.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".