Bibliographic record
Abstract
In recent years, there have been numerous unprecedented technological advances in the field of molecular biology; these include DNA sequencing, mass spectrometry of proteins, and microarray analysis of mRNA transcripts. Perhaps, however, it is the area of genomics, which has now generated the complete genome sequences of more than 100 poxviruses, that has had the greatest impact on the average virology researcher because the DNA sequence data is in constant use in many different ways by almost all molecular virologists. As this data resource grows, so does the importance of the availability of databases and software tools to enable the bench virologist to work with and make use of this (valuable/expensive) DNA sequence information. Thus, providing researchers with intuitive software to first select and reformat genomics data from large databases, second, to compare/analyze genomics data, and third, to view and interpret large and complex sets of results has become pivotal in enabling progress to be made in modern virology. This chapter is directed at the bench virologist and describes the software required for a number of common bioinformatics techniques that are useful for comparing and analyzing poxvirus genomes. In a number of examples, we also highlight the Viral Orthologous Clusters database system and integrated tools that we developed for the management and analysis of complete viral genomes.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".