Bibliographic record
Abstract
The appearance for viruses that evolve to adapt to a new living niche often reflect on viral sequence changes. Fixation of these changes may require a long time through repeated transmission, thereby rendering a reduced size of an effective population harboring dominant alterations in their sequence spaces. Those approaches, with which we can closely monitor and survey the transient changes of viral sequences over the longer timescales, thus become a requisite to better understand the evolution of viral pathogenicity. Molecular barcodes are a powerful and practical molecular tool to individually label sequences, allowing for correcting sequencing errors and identifying true mutants of interest with a single nucleotide resolution. Molecular barcoding has also been implemented as a useful approach to study several zoonotic viruses. In this review, the emphasis will not only be limited to summarize current studies focusing on viral pathogenesis and fitness; we will also propose ideas that molecular barcodes can be used to execute surveillance of changes of viral sequences. We believe that this review will be helpful for the readers to better understand the rationale and the usage of molecular barcodes and the perspectives of what molecular barcodes can do for fighting upcoming emerging infectious diseases.
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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".