Publishing volumes in major databases related to Covid-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The SARS-CoV-2 virus, which causes Covid-19, induced a global pandemic for which an effective cure, either in the form of a drug or vaccine, has yet to be discovered. In the few brief months that the world has known Covid-19, there has been an unprecedented volume of papers published related to this disease, either in a bid to find solutions, or to discuss applied or related aspects. Data from Clarivate Analytics’ Web of Science, and Elsevier’s Scopus, which do not index preprints, were assessed. Our estimates indicate that 23,634 unique documents, 9960 of which were in common to both databases, were published between January 1 and June 30, 2020. Publications include research articles, letters, editorials, notes and reviews. As one example, amongst the 21,542 documents in Scopus, 47.6% were research articles, 22.4% were letters, and the rest were reviews, editorials, notes and other. Based on both databases, the top three countries, ranked by volume of published papers, are the USA, China, and Italy while BMJ, Journal of Medical Virology and The Lancet published the largest number of Covid-19-related papers. This paper provides one snapshot of how the publishing landscape has evolved in the first six months of 2020 in response to this pandemic and discusses the risks associated with the speed of publications.
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.
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 | 0.022 | 0.540 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.113 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it