Microbial diversity in European alpine permafrost and active layers
Bibliographic record
Abstract
Permafrost represents a largely understudied genetic resource. Thawing of permafrost with global warming will not only promote microbial carbon turnover with direct feedback on greenhouse gases, but also unlock an unknown microbial diversity. Pioneering metagenomic efforts have shed light on the permafrost microbiome in polar regions, but temperate mountain permafrost is largely understudied. We applied a unique experimental design coupled to high-throughput sequencing of ribosomal markers to characterize the microbiota at the long-term alpine permafrost study site 'Muot-da-Barba-Peider' in eastern Switzerland with an approximate radiocarbon age of 12 000 years. Compared to the active layers, the permafrost community was more diverse and enriched with members of the superphylum Patescibacteria (OD1, TM7, GN02 and OP11). These understudied phyla with no cultured representatives proposedly feature small streamlined genomes with reduced metabolic capabilities, adaptations to anaerobic fermentative metabolisms and potential ectosymbiotic lifestyles. The permafrost microbiota was also enriched with yeasts and lichenized fungi known to harbour various structural and functional adaptation mechanisms to survive under extreme sub-zero conditions. These data yield an unprecedented view on microbial life in temperate mountain permafrost, which is increasingly important for understanding the biological dynamics of permafrost in order to anticipate potential ecological trajectories in a warming world.
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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".