Does silicon and salicylic acid contribute in the morphophysiology of Schinus terebinthifolia seedlings under flooding?
Bibliographic record
Abstract
Flooding can damage the photosynthetic apparatus and initial growth of Schinus terebinthifolia. We aimed this study evaluates the potential of silicon (Si) and salicylic acid (SA) as mitigating agents on the ecophysiological responses and initial growth of S. terebinthifolia subjected to flooding periods. Seedlings were grown under the following conditions: 1) control (non-flooded): daily irrigation, 2) Flooded (F): storage of seedlings in a 500 L plastic pool, keeping the water depth at ± 2.0 cm above the substrate level, 3) F + 1.0 mM Si, 4) F + 2.0 mM Si, 5) F + 1.5 mM SA, and 6) F + 3.0 mM SA, and evaluated to 15 and 30 days. We observed that flooded seedlings formed hypertrophied lenticels on the stem on the 7th day as a stress adjustment strategy. S. terebinthifolia is sensitive to flooding, although it maintains a stable gas exchange for up to 15 days in this condition. The applications of 1.0 mM Si mitigated the pronounced decrease of gas exchange by 30 days. Using 1.0 mM Si and 3.0 mM SA contributed for integrity of the photosynthetic apparatus and to photochemical activities in the reaction centers, in addition favors to higher seedling biomass and quality under flooding. Foliar application of Si and SA is promising practice for photosynthetic metabolic and initial growth of S. terebinthifolia seedlings under flooded 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) | 0.000 | 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".