Research on optimized design of road space in mixed sections of historical district: a case study of Xi’an, China
Bibliographic record
Abstract
To solve the problems existing in most Chinese historic districts, such as incomplete traffic network planning and unreasonable spatial layout, this paper analyzes the spatial characteristics of motor vehicles in the mixed road section of a historical district and summarizes the application conditions and shortcomings of the model for calculating the Bolankerf lane width and re-calibrates the parameters of the model with the survey data, to determine the formula for calculating the width of motor lanes in different sections of roads. Furthermore, this paper establishes a game model of road space optimization for mixed sections of motor vehicles and non-motor vehicles with the goal of minimizing the generalized travel cost. Finally, by taking the Xiangzimiao historic district as the verification object, this paper draws from the Bolankerf model and the game model to determine the most reasonable road space division plan within blocks.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".