Analysis of Specific Metabolites in Rhizosphere Soil of <i>Panax quinquefolius</i> L with Root Rot Diseases Based on Metabolomics
Bibliographic record
Abstract
The metabolomics method based on gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS) was used to analyze rhizosphere soil differential metabolites, and the rhizosphere soil of healthy ginseng (HS) and root rot ginseng (RS) with 4-year-old were chosen as research objects. 13 metabolites with significant differences ( p <0.05) were screened in the RS vs HS group, including 9 organic acids, 3 carbohydrates, and 1 quinone. Compared with HS group, Lignoceric acid, palmitic acid, cerotinic acid, benzoic acid, oleic acid, heptadecanoic acid, azelaic acid, salicylic acid and 3,4-dihydroxybenzoic acid level was significantly increased ( p <0.05) in RS group, and D-Talose, mannose, N-Acetyl-D-galactosamine and phytol were significantly decreased ( p <0.05). KEGG pathway enrichment analysis found that these differential metabolites were mainly enriched in 10 metabolic pathways, including biosynthesis of unsaturated fatty acids biosynthesis of secondary metabolites, microbial metabolism in diverse environments and degradation of aromatic compounds. Root rot P. quinquefolius L.and healthy P. quinquefolius L. rhizosphere soil have some significantly different metabolites, and these different metabolites may cause the occurrence of P. quinquefolius L. root rot through allelopathic effects. This study provides a theoretical basis for further research on the allelopathy of P. quinquefolius L..
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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".