The Effects of Zinc-Oxide Nanoparticles on Growth Parameters of Corn (SC704)
Bibliographic record
Abstract
Nanoparticles are widely used in various fields like medicine and agriculture. Plant growth is hindered in mineral poor soils. Supplementing mineral poor soils can improve plant growth. One role of nanoparticles in agriculture is stimulating crop growth. In this study, the three different physical forms of ZnO particles in irrigation water were used to supplement mineral poor soil. Their effect on the growth of single cross 704 (SC704) corn was investigated. We studied the effects of ZnO nanocolloid, ZnO nanoparticles, and micrometric ZnO particles. The concentration of nanoparticles in irrigation water was 2 ppm. The results show that the addition of all three ZnO particle types in irrigation water improved shoot dry matter and leaf area index. The best results came from the ZnO nanoparticle treatment which on average, increased the shoot dry matter and leaf area indexes by 63.8% and 69.7% respectively. Based on these results, we can conclude that zinc nanoparticles can improve corn growth and yield in mineral poor soils.
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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) | 0.000 | 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".