An overview of the impact of COVID-19 on the cruise industry with considerations for Florida
Bibliographic record
Abstract
This paper provides an overview of the current state of the world's cruise industry, with a focus on the chronology and the main impacts that the COVID-19 has had on the industry. Florida is presented as a micro context of the pandemic's impacts on the local economies of cruise-dependent regions. As a result of the COVID-19 pandemic and the many infected ships in the first quarter of 2020, the entire cruise industry was stopped and a prohibition on resuming this industry was impose worldwide. This paper presents some of the consequences of stopping the cruise industry and the recommended protocols for resuming. Due to the dramatic impacts on the entire industry, some cruise lines are trying to resume despite the fact that the COVID-19 is not yet under control. The first aim of this paper is to cover the cruise industry and its importance for society, introduce the main facts of the COVID-19 outbreak, and the correlation between cruise ships and the spread of this disease. The second aim is to present the new pattern to resume the cruise industry and its challenges.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".