Importance of Silicon in the Growth of Jatropha curcas L. Plants Irrigated With Salina Water
Bibliographic record
Abstract
The use of silicon in plant cultivation is one of the strategies to mitigate the negative effects of salinity. This study aimed to evaluate the effect of silicate fertilization on the morphophysiological, biochemical and nutritional characteristics of Jatropha curcas L. plants under saline stress. The work was carried out in a greenhouse at the State University of Goiás. The experiment was set up in a completely randomized design in a 5 × 2 factorial arrangement (plants irrigated with salt water with sodium chloride (NaCl) and electrical conductivities equal to 0 dS m-1; 2 dS m-1; 4 dS m-1; 6 dS m-1 and 8 dS m-1 applied at 80 days after emergence (DAE) and absence or presence of silica fertilization of 1 g L-1 with Si applied at 80 and 95 DAE by volume of 30 mL of the solution with the aid of a spray manual), five replicates and experimental plot of one plant per pot. The evaluations were carried out at 130 DAEs. The absence of differences in the concentrations of photosynthetic pigments and visible damages is indicative of the absence of severe toxic effects caused by salinity. The tolerance of Jatropha curcas L. plants to salinity is independent of silicon. The Jatropha curcas L. plant tolerates salinity by minimizing transpiration and remaining hydrated through the water stored in the succulent stem. In addition, the plants control sodium uptake and eliminate toxic compounds through increases in Calcium concentration and antioxidative metabolism respectively.
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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 | 0.001 |
| 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".