Spatial and temporal dynamics of virus occurrence in two freshwater lakes captured through metagenomic analysis
Bibliographic record
Abstract
Viruses are the most abundant microorganisms in the aquatic environment, yet the identification of viruses and assessing their diversity still remains a challenge. Here, we present a robust, routinely usable approach to identify viruses from two freshwater lakes of the lower Great Lakes region, Lake Ontario, and Lake Erie. We collected water samples from six different beaches of these two lakes during the summer period of 2012 and 2013, and separated into three distinct fractions, namely a bacterial fraction, a virus like particle (VLP) fraction, and a fraction of eDNA (environmental DNA). DNA extracted from all three fractions was sequenced and bioinformatic analyses of sequences revealed the presence of viruses from major viral families. The analyzed viral sequences were dominated by bacteriophage sequences, but also contained many plant and animal viruses. Within the context of this study, geographic location does not appear to have a major impact on viral abundance and diversity, since virome composition of both lakes were similar. Comparative analyses between eDNA and viral fractions showed that eDNA can be used in combination with VLP fractions to identify viruses from the environment.
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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 | 0.002 | 0.002 |
| 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".