Enhancing photosynthetic efficiency and antioxidant activity in camelina under cadmium stress through foliar silicon application
Bibliographic record
Abstract
Silicon (Si) plays a pivotal role in enhancing plant resilience against abiotic stressors, yet the precise mechanisms underlying its potential in mitigating cadmium toxicity in camelina remain poorly elucidated. This study focuses on examining the impact of different treatments of foliar Si such as 0 ppm Si with 0 ppm Cd (T0Cd0), 2.5 ppm Si with 0 ppm Cd (T1Cd0), 5 ppm Si with 0 ppm Cd (T2Cd0), 0 ppm Si with 5 ppm Cd (T0Cd1), 2.5 ppm Si with 5 ppm Cd (T1Cd1), and 5 ppm with 5 ppm Cd (T2Cd1) application on two camelina cultivars cultivated under hydroponic conditions of cadmium-induced stress. Cadmium stress significantly impaired various aspects of plant development, including growth parameters, photosynthetic pigments, and the activities of key antioxidant enzymes such as superoxide dismutase, peroxidase, ascorbate peroxidase, and catalase. Additionally, cadmium accumulation was higher in both root and leaf tissues under stress. The Canadian cultivar demonstrated greater susceptibility to cadmium at a concentration of 5 ppm compared to the Australian cultivar. However, the introduction of silicon at a concentration of 5 ppm effectively alleviated the toxic effects of cadmium, with the Australian cultivar showing a more substantial response relative to the Canadian counterpart. These findings highlight silicon’s crucial role in mitigating cadmium toxicity in camelina, enhancing growth and antioxidant defenses under stress.
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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) | 0.001 | 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 | 0.001 |
| 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".