Bibliographic record
Abstract
The COVID-19 pandemic has had a profound impact on the United States and the world. If, as of May 2021, there are more than 33 million Americans in the United States, i.e. each of 10 were infected with severe acute respiratory coronavirus 2 (SARS-CoV-2), which was confirmed by relevant documents, then on August 5 of this year, 35 392 660 confirmed cases of the disease were recorded in the country (200 670 720 worldwide), 615 144 confirmed deaths in the United States (4,263,828 worldwide) and 347,907,126 vaccine doses administered in the United States (4,303,303,528 worldwide). Moreover, it is noted that the true percentage of the infected population may never be known with certainty, given the large proportion of unreported cases, but this number is likely significantly higher than the number reported in official reports. The figures testify to the scale of the existing acute crisis state of affairs in the sensitive social sphere - health care, which is difficult to isolate into one segment of the life of society and the country.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".