Effects of graphene oxide composite additive on the species abundance and diversity of microorganisms in saline–alkali soil
Bibliographic record
Abstract
Saline–alkali soil is common in north China, especially in the Datong district in north Shanxi province. Improving the soil will benefit the environment and society. Graphene oxide (GO) has been shown to benefit agricultural and forest soils. Herein, we explore three different experimental conditions of CK (CK means irrigated with tap water), CJ (CJ means 0.2 g of bacteria addition and irrigated with tap water), and CJ25 (CJ25 means 0.2 g of composited bacteria plus 25.0 mg/L of GO added and irrigated with tap water) for saline–alkali soil by the addition of optimized amounts of GO and external bacteria. Our results show that the addition of 25.0 mg/L GO and microbial agents increases the number of bacteria and fungi in the soil and improves the species abundance of bacteria and fungi in the saline–alkali soil, while having little effect on species richness. The GO and bacterial treatment increased the abundance of Proteobacteria, Actinobacteriota, Chloroflexi, Pseudomonas, Ascomycota, Mortierella, and Fusarium. These bacteria have been shown to produce proteolytic enzymes and cellulases that decompose lignin and cellulose in litter, and thus play important roles in carbon and nutrient cycling. The addition of GO and microorganisms provides a viable way to improve saline–alkali soils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".