Impact of Urea Addition and Rhizobium Inoculation on Plant Resistance in Metal Contaminated Soil
Bibliographic record
Abstract
Legume-rhizobium symbiosis has been heavily investigated for their potential to enhance plant metal resistance in contaminated soil. However, the extent to which plant resistance is associated with the nitrogen (N) supply in symbiont is still uncertain. This study investigates the effect of urea or/and rhizobium (Sinorhizobium meliloti) application on the growth of Medicago sativa and resistance in metals contaminated soil (mainly with Cu). The results show that Cu uptake in plant shoots increased by 41.7%, 69%, and 89.3% with urea treatment, rhizobium inoculation, and their combined treatment, respectively, compared to the control group level. In plant roots, the corresponding values were 1.9-, 1.7-, and 1.5-fold higher than the control group values, respectively. Statistical analysis identified that N content was the dominant variable contributing to Cu uptake in plants. Additionally, a negative correlation was observed between plant oxidative stress and N content, indicating that N plays a key role in plant resistance. Oxidative damage decreased after rhizobium inoculation as the activities of antioxidant enzymes (catalase and superoxide dismutase in roots and peroxidase in plant shoots) were stimulated, enhancing plant resistance and promoting plant growth. Our results suggest that individual rhizobium inoculation, without urea treatment, is the most recommended approach for effective phytoremediation of contaminated land.
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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) | 0.001 | 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".