Seed Germination and Growth of Cucumber (Cucumis sativus): Effect of Nano-Crystalline Sulfur
Bibliographic record
Abstract
The present paper is focused on green synthesis of high purity sulfur nanoparticles (SNPs) and its effect on seed germination and seedling growth of cucumber (Cucumis sativus). Synthesized SNPs were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy equipped with energy-dispersive X-ray spectroscopy (SEM-EDS). The crystalline size of synthesized SNPs as calculated by Scherer equation was 40 nm. SEM analysis of the SNPs is in spherical shape and with a diameter size between 5-80 nm. In the present study, different concentrations of SNPs were used for the treatment of cucumber seeds to study the effect on bioavailability of seed germination and seedling growth of cucumber. The results of this experiment showed that an increase in concentrations of SNPs had significantly increased seed germination and seedling growth of cucumber.
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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".