Viruses of Eukaryotic Algae: Diversity, Methods for Detection, and Future Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The scope for ecological studies of eukaryotic algal viruses has greatly improved with the development of molecular and bioinformatic approaches that do not require algal cultures. Here, we review the history and perceived future opportunities for research on eukaryotic algal viruses. We begin with a summary of the 65 eukaryotic algal viruses that are presently in culture collections, with emphasis on shared evolutionary traits (e.g., conserved core genes) of each known viral type. We then describe how core genes have been used to enable molecular detection of viruses in the environment, ranging from PCR-based amplification to community scale “-omics” approaches. Special attention is given to recent studies that have employed network-analyses of -omics data to predict virus-host relationships, from which a general bioinformatics pipeline is described for this type of approach. Finally, we conclude with acknowledgement of how the field of aquatic virology is adapting to these advances, and highlight the need to properly characterize new virus-host systems that may be isolated using preliminary molecular surveys. Researchers can approach this work using lessons learned from the Chlorella virus system, which is not only the best characterized algal-virus system, but is also responsible for much of the foundation in the field of aquatic virology.
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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) | 0.001 | 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 | 0.001 |
| 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 it