A country-based review in COVID-19 related research developments
Bibliographic record
Abstract
The COVID-19 pandemic has turned our life topsy-turvy. It has bought a massive change in all sectors around the world. A great number of research papers have already been published accounting for various aspects of the COVID-19 issue, owing to the ever-increasing interest in this hot area. The essential data is gathered using the well-known and dependable search engine SCOPUS. We looked at research papers, journals, and reviews from 25 leading countries to highlight a comprehensive study of research output through COVID-19 papers. This study focuses on the top authors, leading articles, and journals from various nations, the percentage of published papers in various fields, and the top collaborative research work from different authors and countries. USA, UK, China, Italy, and India have all made a significant contribution to COVID-19 research. The USA is the leading country followed by UK and China but for H-index China is in the best position. The highest number of papers has been developed in the area of "medicine". The Harvard Medical School of the UK contributed the highest number of papers followed by the University of Toronto of Canada. Professor K. Dhama of India has published the highest number of papers while C. Huang of China received the highest number of citations. It also highlights that several authors have differing opinions on the efficacy of taking the medicine remdesivir. Our research provides a complete and comprehensive image of the virus’s current research status, or in other words, a roadmap of the present research status.
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 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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.026 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".