Metabolomics Profile of Potato Tubers after Phosphite Treatment
Bibliographic record
Abstract
Phosphite (Phi)-based fungicides are used to control the oomycete Phytophthora infestans which causes late blight disease, the most devastating disease in potatoes. In order to examine the effects of Phi-based fungicides on potato tubers through foliar or post-harvest application, a metabolite profiling approach based on gas chromatography coupled to mass spectrometry (GC-MS) has been established. A total of 132 metabolites were detected using the GC-MS approach. Among these, 34 metabolites were identified after normalization and annotated with a compound name with standard mass spectral library. Metabolomic analysis of Phi-treated plants showed significant differences in the levels of many metabolites especially amino acids. Multivariate statistical approaches, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), were employed to explore the relationships between metabolites to detect group differences. A good discrimination between the control and the Phi-treated plants was observed, which demonstrated that significant changes in the metabolite profile have been caused by the two different Phi applications (foliar or post-harvest). This finding suggests that the alteration of specific metabolite levels by accumulation of Phi can lead to resistance against the pathogen.
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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".