Microbial diversity of active layer soil from the Canadian high Arctic
Bibliographic record
Abstract
Active layer is the surface layer of permafrost, which is an extraterrestrial analogue. The repeats of thawing and freezing make active layer an extreme environment for microorganisms. The bacterial and archaeal diversity in an active layer soil from the Canadian high Arctic was analyzed by using a high resolution pyrosequencing analysis. Since last few years, DNA sequencing-based microbial community studies have been widely propagated by the invention of pyrosequencing which produce millions of sequences in a single run (Ronaghi, 2001). This can exclude the time-consuming step such as constructing clone libraries and generates millions of sequencing in a single run. Therefore, tremendous sequence information in a sample can make be visualized the deep insights of microbial population. Total 7,796 bacterial reads for 40 phyla and 245 archaeal reads for 4 phyla were collected, reflecting the high diversity of bacteria. Much higher diversity in bacteria than in archaea was confirmed from the statistical analysis. Among these 40 phyla, 22 phyla were assigned as well-known phyla such as Proteobacteria and Bacteroidetes, whereas 18 phyla assigned as candidate phyla such as TM7, OD1. Proteobacteria (most were from the class Alphaproteobactera and Betaproteobacteria) and Bacteroidetes encompassed the majority of sequences with 67.8%. This is shown a good agreement of soil microbial communities in four different types of soils from Brazil, Florida, Illinois and Canada (Roesch et al., 2007), despite of targeting different region of 16S rRNA gene with this study. A good agreement was found that phylum Proteobacteria was the most dominant with 27.6 – 58.2% in all environments. Bacteroidetes was detected with 30.0% in case of Canadian permafrost, however, around 10% in other environments. The majority of the sequences from rhizosphere soil on Antarctica were affiliated to the Actinobacteria (28.7%) that were typically dominated in soil environment. In case of Archaea, relatively simple community composition was recognized. Euryarchaeota (51.4%) and Thaumarchaeota (46.1%) were major constituents of the archaeal community. The relatives of major species in this study were also isolated from various environments, mainly from freshwater lakes and also from alpine or polar habitats. Taken together, our results indicated that analysis of 16S rRNA gene derived from bar-coded pyrosequencing reads provide the necessary resolution and discrimination to be useful as a next generation platform for microbial diversity study in the Arctic active layer soil.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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 teacher head, 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".