Soil bacterial diversity in the Arctic is not fundamentally different from that found in other biomes
Bibliographic record
Abstract
The severe environmental stresses of the Arctic may have promoted unique soil bacterial communities compared with those found in lower latitude environments. Here, we present a comprehensive analysis of the biogeography of soil bacterial communities in the Arctic using a high resolution bar-coded pyrosequencing technique. We also compared arctic soils with soils from a wide range of more temperate biomes to characterize variability in soil bacterial communities across the globe. We show that arctic soil bacterial community composition and diversity are structured according to local variation in soil pH rather than geographical proximity to neighboring sites, suggesting that local environmental heterogeneity is far more important than dispersal limitation in determining community-level differences. Furthermore, bacterial community composition had similar levels of variability, richness and phylogenetic diversity within arctic soils as across soils from a wide range of lower latitudes, strongly suggesting a common diversity structure within soil bacterial communities around the globe. These results contrast with the well-established latitudinal gradients in animal and plant diversity, suggesting that the controls on bacterial community distributions are fundamentally different from those observed for macro-organisms and that our biome definitions are not useful for predicting variability in soil bacterial communities across the globe.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".