Comparative Effects of Organic and Inorganic Seed Treatments on the Viability and Vigour of Sesame Seeds in Storage
Bibliographic record
Abstract
This study was undertaken to determine the storability of pre-treated sesame seeds and to compare the effects of inorganic and organic seed treatments on the viability and vigour of sesame seeds during storage. Two hundred grammes of sesame seeds were treated separately with recommended dosages of Neem leaf powder (NLP), Dress force powder (DFP), Dry pepper powder (DPP) and untreated seeds being the control; each treatment placed in air-tight container and stored in a wooden cabinet at average ambient conditions of 26.5°C and 80% RH for a period of 18 weeks. Completely randomized design was used for the experiment, with storage time and seed treatment as factors. Samples were drawn from storage at interval of two weeks and tested for seed viability and seedling vigour. Seed germination, seedling vigour index, speed of germination and rate of germination were evaluated. Analysis of variance (ANOVA) was carried out on the data collected, and treatment means were separated using Duncan’s Multiple Range Tests (DMRT). Correlation analysis was also carried out to ascertain the kinds of relationships that exist among the variables evaluated. Observations showed that NLP and DPP treatments had better mean seed germination of 89.53% and 82.35% respectively compared to DFP (46.47%) and control (80.76). NLP also enhanced better seedling vigour index (339.24) throughout the strorage time compared to DFP (99.74). It is therefore recommended that natural botanicals like NLP could be adopted for short and medium term storage of sesame seeds; as it maintaned seed viability and seedling vigour optimally among other treatments.
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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".