RNA viruses in the sea
Bibliographic record
Abstract
Viruses are ubiquitous in the sea and appear to outnumber all other forms of marine life by at least an order of magnitude. Through selective infection, viruses influence nutrient cycling, community structure, and evolution in the ocean. Over the past 20 years we have learned a great deal about the diversity and ecology of the viruses that constitute the marine virioplankton, but until recently the emphasis has been on DNA viruses. Along with expanding knowledge about RNA viruses that infect important marine animals, recent isolations of RNA viruses that infect single-celled eukaryotes and molecular analyses of the RNA virioplankton have revealed that marine RNA viruses are novel, widespread, and genetically diverse. Discoveries in marine RNA virology are broadening our understanding of the biology, ecology, and evolution of viruses, and the epidemiology of viral diseases, but there is still much that we need to learn about the ecology and diversity of RNA viruses before we can fully appreciate their contributions to the dynamics of marine ecosystems. As a step toward making sense of how RNA viruses contribute to the extraordinary viral diversity in the sea, we summarize in this review what is currently known about RNA viruses that infect marine organisms.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".