The effects of tea plants-soybean intercropping on the secondary metabolites of tea plants by metabolomics analysis
Bibliographic record
Abstract
Abstract Background Intercropping, especially with legumes, as a productive and sustainable system, can promote plants growth and improves the soil quality than the sole crop, is an essential cultivation pattern in modern agricultural systems. However, the metabolic changes of secondary metabolites and the growth in tea plants during the processing of intercropping with soybean have not been fully analyzed. Results The secondary metabolomic of the tea plants were significant influence with intercropping soybean during the different growth stages. Especially in the profuse flowering stage of intercropping soybean, the biosynthesis of amino acids was significantly impacted, and the flavonoid biosynthesis, the flavone and flavonol biosynthesis also were changed. And the expression of metabolites associated with amino acids metabolism, particularly glutamate, glutamine, lysine and arginine were up-regulated, while the expression of the sucrose and D-Glucose-6P were down-regulated. Furthermore, the chlorophyll photosynthetic parameters and the photosynthetic activity of tea plants were higher in the tea plants-soybean intercropping system. Conclusions These results strengthen our understanding of the metabolic mechanisms in tea plant’s secondary metabolites under the tea plants-soybean intercropping system and demonstrate that the intercropping system of leguminous crops is greatly potential to improve tea quality. These may provide the basis for reducing the application of nitrogen fertilizer and improve the ecosystem in tea plantations.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".