Comparative Metagenomics of the Active Layer and Permafrost from Low-Carbon Soil in the Canadian High Arctic
Bibliographic record
Abstract
Approximately 87% of the Arctic consists of low-organic carbon mineral soil, but knowledge of microbial activity in low-carbon permafrost (PF) and active layer soils remains limited. This study investigated the taxonomic composition and genetic potential of microbial communities at contrasting depths of the active layer (5, 35, and 65 cm below surface, bls) and PF (80 cm bls). We showed microbial communities in PF to be taxonomically and functionally different from those in the active layer. 16S rRNA gene sequence analysis revealed higher biodiversity in the active layer than in PF, and biodiversity decreased significantly with depth. The reconstructed 91 metagenome-assembled genomes showed that PF was dominated by heterotrophic, fermenting Bacteroidota using nitrite as their main electron acceptor. Prevalent microbes identified in the active layer belonged to bacterial taxa, gaining energy via aerobic respiration. Gene abundance in metagenomes revealed enrichment of genes encoding the plant-derived polysaccharide degradation and metabolism of nitrate and sulfate in PF, whereas genes encoding methane/ammonia oxidation, cold-shock protein, and two-component systems were generally more abundant in the active layer, particularly at 5 cm bls. The results of this study deepen our understanding of the low-carbon Arctic soil microbiome and improve prediction of the impacts of thawing PF.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".