Metagenomic Analysis of Virus Diversity and Relative Abundance in a Eutrophic Freshwater Harbour
Bibliographic record
Abstract
Aquatic viruses have been extensively studied over the past decade, yet fundamental aspects of freshwater virus communities remain poorly described. Our goal was to characterize virus communities captured in the >0.22 µm size-fraction seasonally and spatially in a freshwater harbour. Community DNA was extracted from water samples and sequenced on an Illumina HiSeq platform. Assembled contigs were annotated as belonging to the virus groups (i.e., order or family) Caudovirales, Mimiviridae, Phycodnaviridae, and virophages (Lavidaviridae), or to other groups of undefined viruses. Virophages were often the most abundant group, and discrete virophage taxa were remarkably stable across sites and dates despite fluctuations in Mimiviridae community composition. Diverse Mimiviridae contigs were detected in the samples and the two sites contained distinct Mimiviridae communities, suggesting that Mimiviridae are important algal viruses in this system. Caudovirales and Phycodnaviridae were present at low abundances in most samples. Of the 18 environmental parameters tested, only chlorophyll a explained the variation in the data at the order or family level of classification. Overall, our findings provide insight into freshwater virus community assemblages by expanding the documented diversity of freshwater virus communities, highlighting the potential ecological importance of virophages, and revealing distinct communities over small spatial scales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".