Enumeration of virus particles in aquatic or sediment samples by epifluorescence microscopy
Bibliographic record
Abstract
Microbes and microbial processes are crucial and quantitatively important players in aquatic environments, and viruses as major agents of microbial mortality and nutrient cycling are a key component of aquatic systems. These roles have led to the need to routinely quantify viral abundance as the part of many investigations. Electron microscopy was first used to demonstrate high viral abundances in aquatic samples; by the mid-1990s, the greater accuracy and higher precision of estimates of viral abundance made by epifluorescence microscopy (EFM) were evident. Initially, DAPI (6-diamidino-2-phenylindole) was the stain used to enumerate virus particles in natural samples, but this dye was soon superseded by a new generation of much brighter fluorochromes. This article outlines detailed protocols for enumerating virus particles in aquatic or sediment samples using SYBR Green, SYBR Gold, and Yo-Pro-1. Each of these stains has advantages and disadvantages, but for natural water samples they produce indistinguishable estimates of viral abundance when the appropriate protocols are
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.023 | 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 teacher head, 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".