The functional potential of high Arctic permafrost revealed by metagenomic sequencing, qPCR and microarray analyses
Bibliographic record
Abstract
The fate of the carbon stocked in permafrost following global warming and permafrost thaw is of major concern in view of the potential for increased CH(4) and CO(2) emissions from these soils. Complex carbon compound degradation and greenhouse gas emissions are due to soil microbial communities, but no comprehensive study has yet addressed their composition and functional potential in permafrost. Here, a 2-m deep permafrost sample and its overlying active layer soil were subjected to metagenomic sequencing, quantitative PCR (qPCR) and microarray analyses. The active layer soil and the 2-m permafrost microbial community structures were very similar, with Actinobacteria being the dominant phylum. The two samples also possessed a highly similar spectrum of functional genes, especially when compared with other already published metagenomes. Key genes related to methane generation, methane oxidation and organic matter degradation were highly diverse for both samples in the metagenomic libraries and some (for example, pmoA) showed relatively high abundance in qPCR assays. Genes related to nitrogen fixation and ammonia oxidation, which could have important roles following climatic change in these nitrogen-limited environments, showed low diversity but high abundance. The 2-m permafrost showed lower abundance and diversity for all the assessed genes and taxa. Experimental biases were also evaluated using qPCR and showed that the whole-community genome amplification technique used caused representational biases in the metagenomic libraries by increasing the abundance of Bacteroidetes and decreasing the abundance of Actinobacteria. This study describes for the first time the detailed functional potential of permafrost-affected soils.
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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) | 0.001 | 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".