pH affects bacterial community composition in soils across the Huashan Watershed, China
Bibliographic record
Abstract
To investigate soil bacterial richness and diversity and to determine the correlations between bacterial communities and soil properties, 8 soil samples were collected from the Huashan watershed in Anhui, China. Subsequently, 454 high-throughput pyrosequencing and bioinformatics analyses were performed to examine the soil bacterial community compositions. The operational taxonomic unit richness of the bacterial community ranged from 3664 to 5899, and the diversity indices, including Chao1, Shannon-Wiener, and Faith's phylogenetic diversity ranged from 7751 to 15 204, 7.386 to 8.327, and 415.77 to 679.11, respectively. The 2 most dominant phyla in the soil samples were Actinobacteria and Proteobacteria. The richness and diversity of the bacterial community were positively correlated with soil pH. The Mantel test revealed that the soil pH was the dominant factor influencing the bacterial community. The positive modular structure of co-occurrence patterns at the genus level was discovered by network analysis. The results obtained in this study provide useful information that enhances our understanding of the effects of soil properties on the bacterial communities.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".