Influence of silicon doped biochar on germination and defense mechanisms of pea ( <i>Pisum sativum</i> L.) under copper and salinity stresses
Bibliographic record
Abstract
Soil pollution is an increasing environmental problem in all developing countries. Salinity and heavy metal stress increase the oxidative stress in plants and lead to the increased production of reactive oxygen species, decreases the production of anti-oxidant enzymes and decreases the rate of plant growth. In the current study, pea plants were grown in the botanical garden GCU, Lahore under stress of salinity and copper. In second stage, these plants were given the treatments of silicon nanoparticles and silicon nano-doped biochar. The salinity and copper effects were observed on different parameters of plant growth. Silicon nanoparticles and silicon-doped biochar proved to be the most effective in improving seedling fresh weight (70%) shoot length (68%), vitality index (82%), germination percentage (16%), seed vigor index (38%), and chlorophyll content (46%) under salinity and copper stress. Biochar, on the other hand, effectively improved root length (37%), total seedling length (55%), germination index (10%), and catalase (21%). Silicon nanoparticles and biochar proved to be most effective in decreasing total phenolics (11%) and total protein content (13%). Therefore, it is concluded that the application of Si-NPs and silicon-doped biochar can enhance plants resistance against saline-sodic and metal contaminated soil and can mitigate these stresses.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".