Seasonal variations of phage life strategies and bacterial physiological states in three northern temperate lakes
Bibliographic record
Abstract
The current consensus concerning the prevalence of lytic and lysogenic phage life cycles in aquatic systems is that the host physiological state may influence viral strategies, lysogeny being favoured when hosts have reduced metabolic rates. We explored this hypothesis, by following phage cycle dynamics, host physiological state and metabolic activity over an annual cycle in three lakes subjected to strong seasonal fluctuations, including 4-5 months of ice cover. We observed marked seasonal dynamics of viral and bacterial communities, with low bulk and cell-specific bacterial metabolism in winter, and a dramatic increase in injured bacteria under the ice cover in all lakes. This period was accompanied by contrasting patterns in the proportion of lysogenic cells. In the eutrophic lake, times of low bacterial metabolic rates and high proportion of damaged cells corresponded to highest levels of lysogeny, supporting the notion that hosts are a 'refuge' for viruses. In the two unproductive lakes, peaks of injured cells corresponded to a minimum of lysogeny, suggesting an 'abandon the sinking ship' response, where the prophage replicates before the loss of genome. We suggest that these diverging responses to the host physiological state are not contradictory, but rather that there may be thresholds of cell stress and metabolic activity leading to one or the other response.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".