Bibliographic record
Abstract
Welcome to the World Journal of Virology (WJV), a new member of the World Journal Series. The World Journal Series was first launched as a peer-reviewed scientific journal covering aspects of research, diagnostics and clinical practice in biomedicine in 1995. WJV is an online and open-access peer-reviewed periodical focusing on virology. WJV covers a variety of topics in different areas of virology, including advances in basic research, updates in nomenclature, the development of novel diagnostic assays, the epidemiology of viral disorders and, new developments in the clinical management of viral diseases, including new vaccines and antiviral therapeutics. The purpose in launching the WJV is to promote knowledge exchange related to the classic human viruses as well as newly emerging viruses and their associated clinical disorders. Continually updating knowledge in a timely manner in this field where information related to the unceasing evolution of viruses is becoming available at a rapid pace is challenging. Thanks to the World-Wide-Web we are able to provide a podium for all authors and readers of WJV to address this challenge. I would like to acknowledge the Baishideng publisher, the members of the editorial board, and all contributing authors involved in this inaugural issue of the WJV. I sincerely hope all readers, i.e. future contributing authors, will like WJV and we look forward to your input in assisting WJV to grow and mature.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.012 | 0.032 |
| Insufficient payload (model declined to judge) | 0.023 | 0.023 |
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".