Can oxidative potential be a plant risk indicator for heavy metals contaminated soil? Analysis of ryegrass (Lolium perenne L.) metabolome based on machine learning
Bibliographic record
Abstract
Evaluating the plant risk of soil pollution by plant physiological indices usually requires a long cycle and has significant uncertainty. In this study, oxidative potential (OP) of the in situ heavy metal contaminated soils was measured by the dithiothreitol method. The oxidative stress response of the model plant ryegrass ( Lolium perenne L. ) induced by heavy metal contaminated soil was evaluated by the biomarkers, including superoxide dismutase and total antioxidant capacity. The comprehensive biomarker response index has a significant exponential correlation with the OP of soil ( r = 0.923, p < 0.01) in ryegrass. Metabolomics analysis also showed a significant relationship of the metabolic effect level index of amino acids and sugars with OP. Random forest was selected from four machine learning models to screen the metabolites most relevant to OP, and Shapley additive explanations analysis was used to explain the contribution and the influence direction of the features on the model. Based on the selected 20 metabolites, the metabolic pathways most related to OP in plants, including alkaloid synthesis and amino acids metabolism, were identified. Compared to the plant physiological indices, OP is a more stable and faster indicator for the plant risk assessment of heavy metals contaminated soil. • Oxidative potential (OP) is exponential correlated with the comprehensive biomarker response index. • OP was significantly related to the metabolic effect level indexes of amino acids and sugars. • Top 20 important metabolites in ryegrass contributed to OP was screened based on random forest. • The metabolic pathways in ryegrass most related to OP were identified.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".