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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".