Adaptive evolution of viruses infecting marine microalgae (haptophytes), from acute infections to stable coexistence
Bibliographic record
Abstract
Collectively known as phytoplankton, photosynthetic microbes form the base of the marine food web, and account for up to half of the primary production on Earth. Haptophytes are key components of this phytoplankton community, playing important roles both as primary producers and as mixotrophs that graze on bacteria and protists. Viruses influence the ecology and diversity of phytoplankton in the ocean, with the majority of microalgae-virus interactions described as 'boom and bust' dynamics, which are characteristic of acute virus-host systems. Most haptophytes are, however, part of highly diverse communities and occur at low densities, decreasing their chance of being infected by viruses with high host specificity. Viruses infecting these microalgae have been isolated in the laboratory, and there are several characteristics that distinguish them from acute viruses infecting bloom-forming haptophytes. Herein we synthesise what is known of viruses infecting haptophyte hosts in the ocean, discuss the adaptive evolution of haptophyte-infecting viruses -from those that cause acute infections to those that stably coexist with their host - and identify traits of importance for successful survival in the ocean.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".