Efficient and predictable recovery of viruses from water by small scale ultrafiltration systems
Bibliographic record
Abstract
Current methods to concentrate viruses from large volumes of water are prone to inconsistent results and are costly and complex procedurally. Ultrafiltration can utilize size exclusion rather than adsorption and (or) elution to concentrate viruses and, therefore, may offer greater flexibility in developing methods that can provide more consistent recoveries among different viruses and widely varying water conditions. Two small scale ultrafiltration systems (hollow fiber and tangential flow) were tested with a virus suspended in 2 L of reagent grade, tap, ground, or surface water. Three model viruses were used (bacteriophages PP7 and T1 and poliovirus) to compare and characterize the recovery of viruses with the two ultrafiltration systems. Pretreatment of the ultrafilters with blocking agents and the use of elution agents can serve to prevent viral adsorption to the filter surface or to elute bound virus and keep viral agents suspended in the retentate. The use of a blocking and elution step concentrated viruses (>60% recovery) from widely varying water qualities, including surface water, such that a single method can be used to efficiently concentrate viruses from all of the water types tested. Both ultrafiltration systems appear to be able to efficiently recover viruses; however, the hollow fiber systems provided slightly better results in the 2-L volumes tested.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".