Heterogeneous viral contribution to dissolved organic matter processing in a long-term macrocosm experiment
Bibliographic record
Abstract
Viruses saturate environments throughout the world and play key roles in microbial food webs, yet how viral activities affect dissolved organic matter (DOM) processing in natural environments remains elusive. We established a large-scale long-term macrocosm experiment to explore viral dynamics and their potential impacts on microbial mortality and DOM quantity and quality in starved and stratified ecosystems. High viral infection dynamics and the virus-induced cell lysis (6.23–64.68% d−1) was found in the starved seawater macrocosm, which contributed to a significant transformation of microbial biomass into DOM (0.72–5.32 μg L−1 d−1). In the stratified macrocosm, a substantial amount of viral lysate DOM (2.43–17.87 μg L−1 d−1) was released into the upper riverine water, and viral lysis and DOM release (0.35–5.75 μg L−1 d−1) were reduced in the mixed water layer between riverine water and seawater. Viral lysis was stimulated at the bottom of stratified macrocosm, potentially fueled by the sinking of particulate organic carbon. Significant positive and negative associations between lytic viral production and different fluorescent DOM components were found in the starved and stratified macrocosm, indicating the potentially complex viral impacts on the production and utilization of DOM. Results also revealed the significant viral contribution to pools of both relatively higher molecular weight labile DOM and lower molecular weight recalcitrant DOM. Our study suggests that viruses have heterogeneous impact on the cycling and fate of DOM in aquatic environments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".