Highly Cited Works In Covid-19 - The Global Perspective
Bibliographic record
Abstract
Research papers published covid-19 during 1989-2020 and cited at least 100 times. It is founded that 72 countries contributed a total 1000 highly cited research publications and received 227766 Citations (Average Citation Per Articles is 227.766 and Cited References 51830). United States of America (USA) topped in the table with highest citations (86603) for 432 publications followed by China with 83829 citations for 280 Publications, United Kingdom (UK) with 29372 citations for 122 publications. It is also important to note that the top 8 countries have more than 10,000 citations that include Netherlands, Germany, Canada, France, and Saudi Arabia. India stands in the list with 500 citations. University Hong Kong has the most citations (28278) followed by Chinese Academy of Sciences with 14513 GCS, Huazhong University of Science & Technology with 12294 Citations and Wuhan University with 10392 citations. LANCET’ is the journal which has got the maximum citations of 22221 (41 records) out of the total 249 Journals followed by ‘Journal of Virology’ with 22039 citations (139 records) ‘NEW ENGLAND JOURNAL OF MEDICINE’ with 21213 citations, ‘PNAS’ with 11216. The prominent journals ‘Nature’ and ‘Science’ share the table with 9639 & 9613 citations respectively. It will surprise few recently published (Feb 2020, April 2020, March 2020) that methods papers lead the list of the most cited scientific papers. “The Huang CL, Wang YM, Li XW, Ren LL, Zhao JP, et al.,” leads with 3915 Citations (Huang CL, Wang YM, Li XW, Ren LL, Zhao JP, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. LANCET. 2020 FEB 15; 395 (10223): 497-506). Notably, Institutes from China dominating in top 10 list, University Hong Kong has the most citations (28278) followed by Chinese Academy of Sciences with 14513 Citations.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.038 | 0.069 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".