Bibliographic record
Abstract
For several decades, flow cytometry has been a common approach to analyze cells and sort them to near-purity. It enables one to probe inner cellular molecules, surface receptors, or infected cells. However, the analysis of smaller entities such as viruses and exocytic vesicles has been more difficult but is becoming mainstream. This has in part been due to the development of new instrumentation with resolutions below that of conventional cytometers. It is also attributed to the several means employed to fluorescently label viruses, hence enabling them to stand out from similarly sized particles representing background noise. Thus far, more than a dozen different viruses ranging in size from 40 nm to giant viruses have been probed by this approach, which was recently dubbed "flow virometry." These studies have collectively highlighted the breadth of the applications of this method, which, for example, has elucidated the maturation of dengue virus, served as quality control for vaccinia vaccines, and enabled the sorting of herpes simplex virus discrete viral particles. The present review focuses on the means employed to characterize and sort viruses by this powerful technology and on the emerging uses of flow virometry. It similarly addresses some of its current challenges and limitations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".