Assessing the diversity of freshwater bacteria and viruses
Bibliographic record
Abstract
Microbial water communities are a complex consortium of bacteria, viruses and protozoa. These complex microbial and viral communities may contain pathogens that cannot be detected by conventional methods. Next-generation sequencing (NGS) offers the potential to exhaustively characterize all microbial and viral components of a given water sample and facilitates the identification and quantification of pathogens of interest. The goals of this thesis were to assess the dynamics and the diversity of freshwater bacterial and viral communities of the lower Great Lakes region and identify pathogenic bacterial and viral species. We first assessed the diversity of viral communities in six different beaches of Lake Ontario and Lake Erie, two of the largest freshwater reservoirs in North America. We employed a robust and routinely applicable approach that can provide a comprehensive analysis of bacterial and viral community composition. Our analysis suggests that the viral communities of the lower Great Lakes region are dominated by bacteriophages but also contained viruses of plants and animals. Exhaustive characterization of bacterial communities indicates that the bacterial community composition is highly diverse, and the diversity differs between recreational waters and beach sands. In addition, we identified sequences of pathogens that are not currently included in traditional water monitoring schemes in both recreational water and beach sand. To investigate the impact of spatiotemporal and environmental factors on the distribution of bacterial species, we employed a computational approach and our analysis suggests that dissolved oxygen (DO) level is strongly associated with bacterial community diversity. Using a computational approach, we have also identified habitat-specific bacterial species and a possible link between inter-connected habitats. The findings of this thesis aid in our understanding of bacterial and viral community diversity in recreational waters and provide useful information to water quality decision makers.
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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 | 0.001 |
| 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 | 0.000 |
| 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".