Improvement in gravel-mulched land soil nutrient and bacterial community diversity with Lonicera japonica
Bibliographic record
Abstract
Gravel-mulched land in China suffers from poor natural resources and fragile ecological environment, posing a challenge to effective restoration of ecological function. Lonicera japonica, a traditional Chinese herb used for treating human diseases, is a highly adaptable and resilient plant species, can effectively improve the soil properties, and may have important implications for the ecology and economy of gravel-mulched land. A study was conducted in a gravel-mulched field to measure the impact of planting the L. japonica (including control (CK), 1-year, 2-year, and 4-year cultivation of plants) on (i) dynamic changes in soil nutrient and enzyme activity properties, and (ii) soil rhizosphere microbial community structure characteristics. We found that the concentration of soil organic carbon, available nitrogen, available phosphorus and available potassium in L. japonica soil after cultivation for 1–4 years increased by 11–409%. The urease, phosphatase and catalase activities were increased by 11–560%, with the highest nutrient concentration and enzyme activity in 4-year plants. The pH value gradually decreased after cultivation. The improved soil environments increased soil bacterial community diversity. Planting L. japonica significantly increased the bacterial ACE, Chao1 index, Simpson index, and Shannon-Wiener index. The Firmicutes, Proteobacteria and Bacteroidetes were observed in dominant phyla. The relative abundance of eight genera, including Streptococcus, Veillonella and Rothia, was significantly reduced by more than 1%. Taken together, these soil indicators suggest that planting L. japonica in the short term would be a cost-effective strategy to combat soil degradation in a gravel-mulched ecosystem.
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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".