COVID-19: what are the precautionary measures that you do if you travel to a country with the epidemic?
Bibliographic record
Abstract
Several countries have reported a sizeable increase in the number of cases of the COVID-19 between intimate partners particularly during the lockdown phase of the pandemic, such as Canada, China, the USA, and most EU countries. If you think to travel to one of the epidemic countries, you must take into consideration some precautionary measures before departure. 1- Listening to home news: Care of rises in newly infected cases and consider how the local verdict addresses the issues. If you have on the trip and no trust that the government can restrain the virus effectively, you have to cancel your trip. 2- Follow the foreign office: They ought to have the latest information about the prevalence of disease and how can give counsel accordingly. 3- Monitor health daily: For example, if you have suffered from a weak immune system, long-term conditions of chronic diseases such as heart failure, lung or renal diseases, any types of cancers, or diabetes, you may likely develop severe symptoms if you are exposed to infection. Therefore, canceling the trip is a better option to reduce the exposure rate.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".