Effects of Nano-Silicon Spraying on Physiological Parameters of Bean Varieties Under Saline Stress Conditions
Bibliographic record
Abstract
The winter 2022-2023 field experiment in Babil Governorate's Al-Musayyib project region studied how nano-silicon spraying on three bean varieties and sodium chloride salt stress affected crop physiological parameters.A randomized complete block design was used in this factorial experiment with three replications and three factor.The first factor included four Bean varieties were Local, Aquadulce, and Lue Deotono.Second factor was nano-silicon concentrations (0, 2, and 4) ml L -1 .The third factor is sodium chloride salt stress (0, 75, 150) mmol L -1 .The results showed that the Aquadulce variety was superior in plant height and leaf area, total chlorophyll, peroxidase, and catalase enzyme activity, were 112.76 cm, 3319 cm 2 , 17.01 mg 100 g -1 fresh weight, 15.32 absorption unit min -1 g -1 , and 8.58 absorption unit min -1 g -1 , respectively.As for spraying with nanosilicon, the concentration exceeded (4 ml L -1 ) for the mentioned characteristics, with averages of 109.58 cm, 3161 cm 2 , 18.55 mg 100 g -1 fresh weight, 16.89 absorption unit min -1 g -1 , and 9.10 absorption unit min -1 g -1 , respectively.It was also noted that salt stress had a significant effect on the traits under study, as the treatment (75 mmol L -1 ) excelled in the content of proline acid with an average of 1.31 µmol g -1 , and the treatment (150 mmol L -1 ) excelled in the content of the POD and CAT enzymes with averages of 15.67 and 8.76 absorption unit min -1 g -1 , respectively, it was also observed that the control treatment was superior in the remaining traits.
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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".