One Year with COVID-19: The Impact on Air Transportation throughout 2020
Bibliographic record
Abstract
Air transportation, in particular, has faced unprecedented effects by COVID-19 in terms of flight cancellations and airline bailouts; some argue that the air transportation sector is probably among the hardest hit. In this study, we explore the impact of COVID-19 on air transportation as a networked system throughout the year 2020, while taking the unaffected year 2019 as reference. Exploiting recently developed techniques in data science and network science, we analyzed the temporal evolution of air transportation networks at several scales of fractality, including airports, countries, and continents. Our study provides a comprehensive, data-driven analysis, enhanced with pointers into the recent literature, dissecting the impact of the COVID-19 on aviation as a networked system. It is hoped that this work not only improves understanding of COVID-19, but also gives anchor points on how to better handle future pandemics.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.015 | 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 teacher head, 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".