A metagenomic study of the bacteria in snow algae microbiomes
Bibliographic record
Abstract
The bacterial communities found in snow algae blooms have been described in terms of their 16S rRNA gene community profiles, but little information exists on their metabolic potential. Previously, we reported that several bacterial taxa are common across snow algae blooms in the southwestern mountains of the Coast Range in British Columbia, Canada. Here, we further this work by reporting a partial bacterial metagenome from the same snow algal microbiomes. Using shotgun metagenomic data, we constructed metagenomically assembled bacterial genomes (MAGs). Of the total 54 binned MAGs, 28 were bacterial and estimated to be at least 50% complete based on single copy core genes. The 28 MAGs fell into five classes: Actinomycetia, Alphaproteobacteria, Bacteroidia, Betaproteobacteria, and Gammaproteobacteria. All MAGs were assigned to a class, 27 to an order, 25 to a family, 18 to a genus, and none to species. MAGs showed the potential to support algal growth by synthesizing B-vitamins and growth hormones. There was also widespread adaptation to the low oxygen environment of biofilms, including synthesis of high-affinity terminal oxidases and anaerobic pathways for cobalamin synthesis. Also notable were the absence of N2 fixation, and the presence of incomplete denitrification pathways suggestive of NO signalling within the microbiome.
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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 | 0.002 |
| 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".