The Study of How Open Community Based on Combination Weighting Method Influence on Road Capacity
Bibliographic record
Abstract
The paper studies whether the opening of community can improve the road capacity. First of all, based on the consideration of road space and traffic quality, the evaluation indexes are: road network density, road network connectivity, non-linear coefficient, traffic volume of main road beside community, delay time of intersection, travel time consumption and vehicle accessibility. Secondly, the weight of the index is calculated by the method of relative comparison, the variation coefficient method and the entropy weight method. Then, we sort the three weights, using the correlated coefficient and degree of spearman rank to combine the three weights, so as to get combined weights of various index and achieve the effect which makes good use of the method. Finally, we combine indicators and weights, establish a comprehensive evaluation model, and assess the road capacity before and after the opening of the road. Through the calculation, it is found that the comprehensive evaluation index after the opening of the community is higher than that before the opening, that is, the positive impact on the surrounding roads.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".