Effects of soil moisture on soil viral reproductive strategies in an agricultural soil
Bibliographic record
Abstract
Abstract Genomic evidence suggests that lysogenic viruses significantly influence the evolution of their host communities and soil microbial ecology and functionality. However, the response of soil viral reproductive strategies (VRS) to environmental factors, in particular soil water stress, remains poorly understood. We investigated this by employing a laboratory microcosm incubation system with different soil moisture levels (30%, 60% and 90% field capacity). Our study focused on soil biochemical properties, bacterial and viral populations, lysogenic fractions and virus/bacteria ratio (VBR). The results showed that soil moisture significantly affected bacterial and viral counts, lysogenic fractions and VBR ( p < 0.01), with bacterial counts increasing and viral counts decreasing with increasing soil moisture. The lysogenic fraction peaked at low moisture, suggesting a shift in viral strategy under hydration stress, which may affect virus‐bacteria interactions and nutrient dynamics, enhancing host adaptability. Analyses using correlation, random forest and structural equation modelling identified soil moisture as the dominant factor shaping VRS by altering nutrient availability and host population. These findings provide a new insight into microbial regulation of feedback to environmental change from the life history strategies of soil viruses.
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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".