Study on the Problems and Countermeasures of the Development of Aviation Core Industry to Promote the Construction of Airside Economy in Henan Province Based on Entropy Weight Method and Grey Situation Decision-making
Bibliographic record
Abstract
As the trend of economic globalization continues to develop, air transport, as a fast and convenient mode of transportation, is playing an increasingly important role in economic development. This study analyzes the driving force of airside economic construction from four levels: primary influence, secondary influence, derivative influence and permanent influence. It also analyz es the dynamic relationship between the aviation industry and the construction of airside economy. In order to further research on the development of airside economic construction, this paper uses the entropy weight method to optimize the gray situation de cision making theory, and conducts research on the development and countermeasures of airside economic construction in Henan Province. According to the gray decision making effect measurement calculation, it is known that the key construction area of airsi de economy in Henan Province should be selected as H2 area, with the effect measurement score of 0.9789, the highest value. The economic effect achieved by prioritizing the development of tertiary industry or the joint development of secondary and tertiary industries in the construction of airside economy in the H2 area is the highest, with the effect measurement scores of 0.755 and 0.749, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".