Improved Growth and Metabolism of Sunflower via Physical Seed Pretreatments
Bibliographic record
Abstract
Physical pre-sowing seed treatments as low-cost ecofriendly strategy for better growth and yield of food crops are well accredited. This study was conducted to find out the possible role of physical pre-sowing seed treatments to improve growth and metabolism of sunflower. Seeds were subjected to pre-sowing gamma (γ) irradiation (0, 10, 20 and 30 kGy), He-Ne laser (0, 1, 2, 3 min), UV-B (0, 20, 30, 40 min) and magnetic field (0. 0.1 Tesla, 0.2 Tesla, 0.3 Tesla) treatments. At vegetative and flowering stages, photosynthetic pigments, carotenoids, proteins, flavonoids, soluble sugars, reducing sugars, total soluble phenolics and anthocyanins were evaluated. Results indicated that γ-radiation (10 kGy) and He-Ne (at 1 and 2 min) increased leaf biomass. Along with gamma and UV treatments, He-Ne laser and magnetic field treatment also significantly enhanced the achenes per capitulum and seed oil contents. Chlorophyll contents were higher at vegetative stage as compared to flowering stage. Total soluble proteins, total soluble sugars and total phenolics were increased in all treatments at their all levels. Moreover, flavonoid contents were increased by all pre-sowing, but reverse was displayed by anthocyanin at vegetative stage and no effect was noted at flowering stage. However, oil contents showed increase only in He-Ne or magnetic pretreatment and decreased in response to gamma and UV radiation treatment. In conclusion, physical seed pretreatments proved pragmatic option to improve growth and metabolite accumulation especially at vegetative stage in sunflower. Moreover, lower doses of gamma, UV and He-Ne laser and all the doses of magnetic treatment improved the yield in term of achenes per capitulum along with achene oil percentage.
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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".