Diversity of Viruses Infecting Eukaryotic Algae
Bibliographic record
Abstract
Algae are photosynthetic organisms that drive aquatic ecosystems, e.g. fuelling food webs or forming harmful blooms. The discovery of viruses that infect eukaryotic algae has raised many questions about their influence on aquatic primary production and their role in algal ecology and evolution. Although the full extent of algal virus diversity is still being discovered, this review summarizes current knowledge of this topic. Where possible, formal taxonomic classifications are referenced from the International Committee on Taxonomy of Viruses (ICTV); since the pace of virus discovery has far surpassed the rate of formal classification, however, numerous unclassified viruses are discussed along with their classified relatives. In total, we recognized 61 distinct algal virus taxa with highly variable morphologies that include dsDNA, ssDNA, dsRNA, and ssRNA genomes ranging from approximately 4.4 to 560 kb, with virion sizes from approximately 20 to 210nm in diameter. These viruses infect a broad range of algae and, although there are a few exceptions, they are generally lytic and highly species or strain specific. Dedicated research efforts have led to the appreciation of algal viruses as diverse, dynamic, and ecologically important members of the biosphere, and future investigations will continue to reveal the full extent of their diversity and impact.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".