Desk Review of Impact of Coronavirus on the Aviation Sector in the United States
Bibliographic record
Abstract
Desk review in this context is an assessment of global pandemic event of COVID-19 and Omicron specifically as it concerns United States of America. The purpose of this research is to determine the impact of COVID-19 on aviation sector of United States. This is with a view to assess pattern of passengers’ movement and employees’ employment status after the pandemic in the United States’ air transport industry. Downloaded research articles and data from Bureau of Transportation Statistics - United States Department of Transportation were analyzed with descriptive statistics of tables, graphs and histogram. Apart from recorded loss of lives and restriction of movements, demand for air travel and tourism was greatly affected by the pandemic. There was a steady increase in passenger movement by air across South America, Central America, Canada and North America regions of United States. It could be observed that, North America demands for air travel during and a bit after COVID-19 was above all regions followed by Canada with a steady observation for about six months before rise in travel demand. Full time employees recorded in the airline from 2010 to 2022 on monthly basis have not been matched since the pandemic especially from October to December. While acknowledging the roles played by Centre for Diseases Control and World Health Organization to contain the pandemic, their recommendations from history must be followed by all stakeholders in 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".