Snow viruses and their implications on red snow algal blooms
Bibliographic record
Abstract
ABSTRACT Algal blooms can give snowmelt a red color, reducing snow albedo and creating a runaway effect that accelerates snow melting. The occurrence of red snow is predicted to grow in polar and subpolar regions with increasing global temperatures. We hypothesize that these algal blooms affect virus-bacteria interactions in snow, with potential effects on snowmelt dynamics. A genomic analysis of double-stranded DNA virus communities in red and white snow from the Whistler region of British Columbia, Canada, identified 792 putative viruses infecting bacteria. The most abundant putative snow viruses displayed low genomic similarity with known viruses. We recovered the complete circular genomes of nine putative viruses, two of which were classified as temperate. Putative snow viruses encoded genes involved in energy metabolisms, such as NAD + synthesis and salvage pathways. In model phages, these genes facilitate increased viral particle production and lysis rates. The frequency of temperate phages was positively correlated with microbial abundance in the snow samples. These results suggest the increased frequency of temperate virus-bacteria interactions as microbial densities increase during snowmelt. We propose that this virus-bacteria dynamic may facilitate the red snow algae growth stimulated by bacteria. IMPORTANCE Microbial communities in red snow algal blooms contribute to intensifying snowmelt rates. The role of viruses in snow during this environmental shift, however, has yet to be elucidated. Here, we characterize novel viruses extracted from snow viral metagenomes and define the functional capacities of snow viruses in both white and red snow. These results are contextualized using the composition and functions observed in the bacterial communities from the same snow samples. Together, these data demonstrate the energy metabolism performed by viruses and bacteria in a snow algal bloom, as well as expand the overall knowledge of viral genomes in extreme environments.
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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 | 0.000 |
| 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 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".