Bacterial community composition is an important predictor of surface soil fertility across different land use types: a case study in the Three Gorges Reservoir area
Bibliographic record
Abstract
Background: Surface soil is a vital component of terrestrial ecosystems and is of great importance for primary productivity. In Zhangjiachong, a small watershed in Zigui County, central China, human activity and erosion cause extensive surface soil degradation. It is still unclear as to what extent human activity influences soil fertility and soil microorganisms in this area. Methods: Soil samples were collected, during spring and autumn, across a series of land use types with different levels of human activity. We assessed soil fertility and microbial communities using 16S rRNA gene sequencing and Biolog ECO-plates. Results: The results showed that higher levels of human activity were associated with lower soil fertility and microbial metabolic activity, in addition to higher bacterial diversity. Moreover, human activity had negative effects on the relative abundances of Proteobacteria and Acidobacteriota, which were the key drivers of surface soil fertility. Conversely, stronger human activity was associated with lower abundance of Actinobacteriota. This study suggested that human activity had a negative influence on surface soil fertility, and bacterial community composition could be a good predictor of surface soil fertility.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".