Improved method for recovery of bacteriophage from large volumes of water using negatively charged microporous filters
Bibliographic record
Abstract
Current virus-recovery procedures using negatively charged microporous filters provide an inexpensive, reliable method for the recovery and detection of enteroviruses from water and wastewater; however, adjustment of the test samples to pH 3.5 to promote enterovirus adsorption results in significant inactivation of bacteriophage and an inability to simultaneously recover them from large volumes of water using this procedure. Procedures specifically designed for the detection of bacteriophage are currently in use but generally are only effective for small volumes of water. Positively charged filters can be used to recover both enteroviruses and bacteriophage from large volumes of water at neutral pH; however, the filters are expensive. The addition of manganese chloride to test solutions at pH 3.5 prior to filtration through negatively charged Filterite filters allowed for sampling of larger volumes of water by reducing the inactivation of bacteriophage and increasing the recovery of PRD1, MS2, and naturally isolated bacteriophage by a factor of four or five when compared with recoveries from solutions without MnCl2. This method provides an inexpensive, reliable alternative to large-volume bacteriophage recovery procedures that use positively charged filters at neutral pH.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".