A comprehensive method for amplicon-based and metagenomic characterization of viruses, bacteria, and eukaryotes in freshwater samples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Studies of environmental microbiota typically target only specific groups of microorganisms, with most focusing on bacteria through taxonomic classification of 16S rRNA gene sequences. For a more holistic understanding of a microbiome, a strategy to characterize the viral, bacterial, and eukaryotic components is necessary. RESULTS: We developed a method for metagenomic and amplicon-based analysis of freshwater samples involving the concentration and size-based separation of eukaryotic, bacterial, and viral fractions. Next-generation sequencing and culture-independent approaches were used to describe and quantify microbial communities in watersheds with different land use in British Columbia. Deep amplicon sequencing was used to investigate the distribution of certain viruses (g23 and RdRp), bacteria (16S rRNA and cpn60), and eukaryotes (18S rRNA and ITS). Metagenomic sequencing was used to further characterize the gene content of the bacterial and viral fractions at both taxonomic and functional levels. CONCLUSION: This study provides a systematic approach to separate and characterize eukaryotic-, bacterial-, and viral-sized particles. Methodologies described in this research have been applied in temporal and spatial studies to study the impact of land use on watershed microbiomes in British Columbia.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it