Differences in Soil Microbial Diversity between Long-term Continuous Cropping and Rotation of Cherry Tomato ( <i>Lycopersicon esulentum</i> Mill)
Bibliographic record
Abstract
Continuous cropping had negative effects on soil microbial community, while rotation was beneficial to the formation of soil microbial community diversity. However, the difference in composition and diversity of microbial communities are still unclear under two cultivation patterns of long-term cherry tomato (Lycopersicon esulentum Mill) continuous cropping and cherry tomato - rice (Oryza sativa L.) rotation. soils from rice-cherry tomato rotation for 10 years (R10) and continuous cropping cherry tomato for 10 years (C10) were selected to as study objects, and high-throughput sequencing technology was conducted to study the difference under two cultivation patterns. The main objective is to provide a theoretical basis for applying rotation measures to reduce the continuous cropping obstacles of cherry tomato from the perspective of microbial ecology. The Chao1 and ACE indices of soil fungi in C10 were significantly higher than those in R10. The Shannon index of soil bacterial community was significantly greater in C10 than in R10, but that of fungal community was significantly lower in C10 than in R10.The relative abundance of beneficial microorganisms was in the order R10 > C10. the relative abundance of Acidobacteria, Actinobacteria, Candidatus_Solibacter, Bryobacter, Bacillus, Mortierella, Trichoderma, and Penicillium etc. was significantly higher in R10 than in C10. alkali-hydrolyzed nitrogen (AN) and available P (AP) were important factors affecting the bacterial community structure, AP was an important factor affecting the fungal community structure, as indicated by significant positive correlations between the important environmental factors and bacterial and fungal community structure.
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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 | 0.000 | 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".