IMPACT OF SILICON DIOXIDE NANOPARTICLES ON SEEDLING EARLY GROWTH OF LENTIL (LENS CULINARIS MEDIK.) GENOTYPES WITH VARIOUS ORIGINS
Bibliographic record
Abstract
Quick seed germination and stand establishment are significant factors to lentil production under saline soil of arid and semi-arid regions.The application of beneficial nanoparticles during the seed germination has shown a new field of nano-agriculture.The current study was aimed at investigating the potential influences of nano-silicon dioxide (at 1 and 2 mM concentration) on seed germination of lentil (Lens culinaris Medik.)genotypes with different geographical origins under a range of NaCl concentrations (i.e.0, 50, 100 and 150 mM).Results showed that germination significantly delayed by increasing salt stress.However, the rate of decline was variable among the genotypes.Application of 1 mM silicon dioxide nanoparticles (nSiO 2 ) could considerably alleviate the adverse effect of salt stress on germination percentage, root and shoot length, seedling weight, mean germination time, seedling vigour index and cotyledon reserve mobilization.The suppressive impact with higher nSiO 2 concentration (2 Mm) shows the need for cautious application of these particles during seed germination.The best performance was recorded for genotypes originated from Mexico, Syria and Jordan (PI 299127, Syrian Local Large, 78S 26013).Our results suggest that nSiO 2 has favorable effect on lentil seed germination under salinity stress and it can be economic to use suitable concentration of this nanoparticle in the production system under saline conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".