Real-time 4D tracking of airborne virus-laden droplets and aerosols
Bibliographic record
Abstract
Abstract There is currently no real-time airborne virus tracking method, hindering the understanding of rapid virus changes and associated health impacts. Nano-digital in-line holographic microscopy (Nano-DIHM) is a lensless technology that can directly obtain the interference patterns of objects by recording the scattered light information originating from the objects. Here, we provide evidence for real-time physicochemical tracking of virus-laden droplets and aerosols in the air using desktop label-free Nano-DIHM. The virus interference patterns, as single and ensemble particles, were imaged by the Nano-DIHM with 32.5 ms resolution. The next-generation Stingray and Octopus software was used to automate object detection, characterization and classification from the recorded holograms. The detection system was demonstrated to detect active MS2 bacteriophages, inactivated SARS-CoV-2 and RNA fragments, and an MS2 mixture with metallic and organic compounds. This work demonstrates the feasibility of using Nano-DIHM to provide rapid virus detection to improve transmission management in real time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".