Research on the Robustness of Air Transportation System Based on Complex Network
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
Air transportation has the characteristics of fast transportation, high transportation efficiency and independent of ground conditions, so the economic benefits brought by air transportation greatly exceed those of other transportation modes. However, the safety of the air transportation system is constantly challenged. In recent years, the natural environment has changed due to rapid economic development, and meteorological disasters have occurred from time to time. In addition, flight cancellations due to airline aircraft turnarounds, technical aircraft failures, and traffic jams at airport terminals have become commonplace. To address the problem of insufficient stability of China's air transportation system, cities and flights with civil airports existing by the third quarter of 2022 are taken as the research objects, and the network model of China's air transportation system is constructed by using Ucinet software with the cities where airports are located as nodes and regular routes between airports as connecting edges. Then, robustness simulation experiments are conducted by random and intentional disruptions to analyze the extent to which the network can maintain its normal operation after the nodes in the network are down. The conclusions show that the robustness of the Chinese air transport network is better than that of the intentional disturbance in the case of random disturbance. Finally, suggestions for countermeasures that can improve the stability of the air transportation system are given.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it