Coronavirus research output during 2001-2020: A Scientometrics Analysis
Bibliographic record
Abstract
COVID-19 virus originated from Wuhan city of China in December 2019. The emergence of COVID-19 the whole world and severely affected by The United States, China, Brazil, and India etc.. World Health Organization (WHO) declared it as a pandemic in March 2020. Due to COVID-19, a large number of literature published in early 2020. However, very few studies address the impact of published related to literature Coronavirus. In response to the current study conducted and reviewed 20 years' period from 2001- May 2020. A total of 14439 documents were found in the Scopus database, which was published during the study period i.e. 2001- May 2020. The study found that The United States 9973 contributed the highest number of published literature on Coronavirus followed by China. Overall, the USA, China, Germany, The UK, Canada, South Korea accounted for most of the Coronavirus research activity at the global level. Globally, the University of Hong Kong and the Chinese University of Hong Kong ranked with first and second positions in terms of the number of publications contributed to individual institutes. The large quantity of scholarly documents related to Coronavirus has considerably increased in early 2020. December 2019
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.109 | 0.216 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".