To kill or not to kill: The balance between lytic and lysogenic viral infection is driven by trophic status
Bibliographic record
Abstract
Experiments were conducted to investigate spatiotemporal patterns in lytic and lysogenic viral infection using water samples collected on the Canadian Arctic Shelf, southern Beaufort Sea. Viral production (VP) and viral‐induced mortality of bacteria (VMB) were determined using a viral reduction approach during a full seasonal cycle, while the percentage of lysogenic bacteria (PLB) in spring and summer was determined in virus‐reduced samples by induction with mitomycin C. Overall, VP (range: 0.3 × 108‐77 × 108 viruses L−1 d−1), VMB (range: 0.2 × 107‐43 × 107 bacteria L−1 d−1), and PLB (range: 4–38%) displayed marked spatiotemporal variations concomitant with changes in chlorophyll a, bacterial abundance, and production. Highest VP and VMB occurred in summer when the water was warmest, stratified, and most productive, and when viruses removed up to 29% of bacterial standing stock d−1 and released up to 4.3 µg of organic carbon L−1 d−1. In contrast, the highest PLB occurred in spring when the water was colder, well mixed, and oligotrophic. Correlative and regression analyses indicated viral lytic and lysogenic variables were significantly coupled with chlorophyll a and the abundance, production, and growth rate of bacteria, implying that viral lytic and lysogenic lifestyles were dependent on system productivity. Furthermore, lytic VP and the proportion of lysogenized bacteria were inversely related, suggesting a dynamic interplay between viral infection pathways. Lytic infection was more pronounced when system productivity was high, while lysogeny prevailed when system productivity was low. These data demonstrate the important role of viruses in bacterial mortality and carbon cycling in the Arctic Ocean, and show how their effect is influenced by trophic status.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".