Comprehensive Evaluation and Classification of Interchange Diagrammatic Guide Signs’ Complexity
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
The effectiveness of interchange diagrammatic guide signs has significant meaning in traffic safety and driver’s understanding. This paper presented a comprehensive evaluation and classification of interchange diagrammatic guide signs’ complexity. The effectiveness of interchange diagrammatic guide signs relies on how well road users can understand those diagrams. This study tested 37 types of diagrams on the visual recognition complexity degree in three levels, general level, partial level, and detailed level, and finally seven indexes are selected to evaluation and classification of interchange diagrammatic guide signs’ complexity. These indexes can be used to conduct quantitative evaluation and classification. And the result of diagram complexity range is between −1.366 and 2.046, which have a correlation with graph cognition complexity, including perspective of distribution, diagram character, essential element expression manner, and utilization degree, and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:math>-means clustering method was used in the analysis. Based on the presented method, 37 types of diagrams are separated into three categories according to their complexity score: low complexity, medium complexity, and high complexity. This study not only presents a theoretical approach for quantitative evaluation of guide signs’ complexity and effectiveness but also can be a reference for traffic sign design and application.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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