Soil characteristics more strongly influence soil bacterial communities than land-use type
Bibliographic record
Abstract
To gain insight into the factors driving the structure of bacterial communities in soil, we applied real-time PCR, PCR-denaturing gradient gel electrophoreses, and phylogenetic microarray approaches targeting the 16S rRNA gene across a range of different land usages in the Netherlands. We observed that the main differences in the bacterial communities were not related to land-use type, but rather to soil factors. An exception was the bacterial community of pine forest soils (PFS), which was clearly different from all other sites. PFS had lowest bacterial abundance, lowest numbers of operational taxonomic units (OTUs), lowest soil pH, and highest C : N ratios. C : N ratio strongly influenced bacterial community structure and was the main factor separating PFS from other fields. For the sites other than PFS, phosphate was the most important factor explaining the differences in bacterial communities across fields. Firmicutes were the most dominant group in almost all fields, except in PFS and deciduous forest soils (DFS). In PFS, Alphaproteobacteria was most represented, while in DFS, Firmicutes and Gammaproteobacteria were both highly represented. Interestingly, Bacillii and Clostridium OTUs correlated with pH and phosphate, which might explain their high abundance across many of the Dutch soils. Numerous bacterial groups were highly correlated with specific soil factors, suggesting that they might be useful as indicators of soil status.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".