Silicon Application Contributes to Efficient Gas Exchange in Tomato Plants
Bibliographic record
Abstract
The study aimed to analyze the effect of silicon application on tomato crops subjected to different water management conditions. The experiment was conducted in a completely randomized design with two water replacement conditions (60 and 100% of crop evapotranspiration - ETc), four forms of silicon application (no application, full-dose soil application, split soil application and foliar application) with four replicates. When the plants were in the reproductive stage, gas exchange analysis was performed in two periods (critical period before water replacement and after water replacement). The photosynthesis rates, internal CO2, stomatal conductance, transpiration, and instantaneous water use efficiency were determined. The data were subjected to analysis of variance and the means compared by the Tukey test with 5% significance. The application of silicon caused significant variation in gas exchange in tomato leaves, with an increase in rates and an increase in instantaneous water use efficiency. The application of silicon promotes an increase in gas exchange in tomato plants, whether in adequate water conditions or with a deficit in the crop. The application of the element, via soil and foliar, showed a significant increase in gas exchange in tomato plants. The best results were obtained for application via soil, regardless of the water condition or time of evaluation.
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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 | 0.000 |
| 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".