Novel Coronavirus 2019: A Recent Update
Bibliographic record
Abstract
Since last year of December 2019, a virus has been identifying in china’s city of Wuhan, virus name Severe Acute Respiratory Syndrome coronavirus (SARS-CoV-2). 2019 Novel Coronavirus (COVID-19) Disease is a very scary Disease. This disease is a challenge for Human for cure. These virus are effected all over the world’s country like America, Brazil, Turkey, China, Italy, Iran, India, Canada Russia etc., this virus first time reported in relation to the Huainan Seafood Wholesale Market (South China Seafood City Food Market) in Wuhan, China. This market gained media attention after being identified as a point of origin of the 2019–20 coronavirus pandemic. This virus have the common sign & symptoms like pneumonia and show symptoms of fever, headache, joint pain, Common cold, chills, shortness of breath, cough severe pneumonia, dyspnea, renal insufficiency. The detection of 2019- SCoV-like viruses in tiny size, live wild mammals in a market indicates a route of inters species spreading, although the natural loch is not known. This theory assembles a study of the molecular biology fundamental of these infectious agents, with particular prominence on the nature and identify of viral receptors, viral RNA synthesis, and the molecular interactions governing viral assembly.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".