Measurement Method of Comprehensive Transportation Development Quality Based on Transportation Efficiency
Bibliographic record
Abstract
Transportation is the forerunner of economic and social development. Therefore, the high-quality development of comprehensive transportation is of great significance to ensure overall economic and social progress and the smooth implementation of major national strategies. The essence of high-quality development in transportation is to realize the optimal allocation of transportation resources. This study handles two aspects. First, the factors that reflect the quality of comprehensive transportation development are defined and analyzed, and an evaluation system is proposed to build China’s comprehensive transportation development quality with transportation efficiency as the core is proposed, taking into account transportation infrastructure and transportation scale. Second, the static comprehensive evaluation value is calculated by the entropy weight method, and then the incentive control model is constructed by introducing incentive factors to achieve a dynamic comprehensive evaluation of comprehensive transportation development. The research results not only propose new indicators but also evaluate different modes of transportation within the same dimension. The results show that the quality of comprehensive transportation development in China is generally on the rise, but there are obvious regional differences. The proposed model is derived from evaluation cases in transportation-related fields and has not yet been applied in the transportation field. It can help understand the development status of the industry and assist in policy formulation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".