Bibliographic record
Abstract
The COVID-19 pandemic caused by SARS-CoV-2 dominated the year 2020 and has an unprecedented impact on global air transport. This paper tries to make an overall review on the interaction of air transport and the COVID-19 pandemic. Although the confirmed cases were first reported in China, the origin of the pandemic remains uncertain. China was the first country to control the COVID-19 pandemic, and domestic air traffic recovers at a fast pace. Compared to 2019 level, world scheduled capacity, passengers carried, and revenues for 2020 were reduced by 50%, 60%, and $371 billion, respectively. Compared with domestic passenger traffic, international passenger traffic reduces more seats, passengers, and revenues. Because air transport contributes to economic growth, many countries or regions provide aviation bailout. The US provides most direct aid, while China mostly provides policy measures. In the post-COVID-19 period, IATA suggests that accepting vaccinated passengers is the best practice to reopen borders. Air cargo will play an important role in the distribution of COVID-19 vaccines. Meanwhile, air transport should be more digitalized, sustainable, and responsible in the future.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".