Time Series Analysis of China’s Air Passenger Traffic Amid the COVID-19 Pandemic
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
China’s air transportation industry has a great development in recent decades along with economic growth and liberalization. However, the impact of COVID-19 pandemic on China’s civil aviation industry is severe and persistent. The paper discusses the development of China’s air transportation and examines the impact of the pandemic on airline industry. The Autoregressive Distributed Lag (ADL) Model and Granger Causality test will be used to investigate the relationships between China’s air passenger traffic and its potential factors including the new COVID-19 cases in China, the Consumer Price Index and unemployment rate in China. The investigation concludes that China’s air passenger traffic is closely related to its own past observations, and the past observations of new infected cases in China is significant in forecasting air passenger traffic. The ADL model forecasts China’s air passenger traffic will have an increasing trend in the following years, but it will still require longer time to recover from the COVID-19 impact.
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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 | 0.001 | 0.004 |
| 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) | 0.007 | 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