Diversity and origins of bacterial and archaeal viruses on sinking particles reaching the abyssal ocean
Bibliographic record
Abstract
Sinking particles and particle-associated microbes influence global biogeochemistry through particulate matter export from the surface to the deep ocean. Despite ongoing studies of particle-associated microbes, viruses in these habitats remain largely unexplored. Whether, where, and which viruses might contribute to particle production and export remain open to investigation. In this study, we analyzed 857 virus population genomes associated with sinking particles collected over three years in sediment traps moored at 4000 m in the North Pacific Subtropical Gyre. Particle-associated viruses here were linked to cellular hosts through matches to bacterial and archaeal metagenome-assembled genome (MAG)-encoded prophages or CRISPR spacers, identifying novel viruses infecting presumptive deep-sea bacteria such as Colwellia, Moritella, and Shewanella. We also identified lytic viruses whose abundances correlated with particulate carbon flux and/or were exported from the photic to abyssal ocean, including cyanophages. Our data are consistent with some of the predicted outcomes of the viral shuttle hypothesis, and further suggest that viral lysis of both autotrophic and heterotrophic prokaryotes may play a role in carbon export. Our analyses revealed the diversity and origins of prevalent viruses found on deep-sea sinking particles and identified prospective viral groups for future investigation into processes that govern particle export in the open ocean.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".