Analysis of Specific Metabolites in Rhizosphere Soil of <i>Panax quinquefolius</i> L with Root Rot Diseases Based on Metabolomics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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..
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it