Resource dynamics during infection of <i>Micromonas pusilla</i> by virus MpV‐Sp1
Bibliographic record
Abstract
Viruses infecting marine phytoplankton drive phytoplankton diversity, terminate blooms and shuttle genetic material. Assessments of the scale of viral impacts on trophic networks are, however, speculative. We investigated fluxes of DNA between host and virus during infection of the prasinophyte alga Micromonas pusilla by phycodnavirus MpV SP1. Under a light-dark regimen, viral genomes accumulated to a transient peak within 24 h, at the expense of both host DNA synthesis and nuclear DNA. Viral genome abundance then declined soon after host lysis. This release of a phosphate-rich nucleotide pool during viral infection of phytoplankton should be considered in trophic models. Lysis required light and was stalled in darkness, meanwhile viral genome replication proceeded slowly in the dark. Viral exploitation of this host is therefore only partially light-dependent and infected phytoplankton are poised to lyse at dawn or if mixed to the photic zone. The chloroplast genome remained intact until lysis, indicating that either this DNA pool is inaccessible or the virus spares the chloroplast for its energy and reductant generation. The photochemical turnover of residual Photosystem II complexes accelerated during lysis, indicating that events in late infection heighten demands on the remaining host photosynthetic systems, consistent with the light dependency of lysis.
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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) | 0.000 | 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) | 0.001 | 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 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".