Genetic Engineering of Local Cayenne Pepper (Capsicum frustescens L.) Through Breeding With Multigamma Irradiation Methods to Obtain Superior Offspring
Bibliographic record
Abstract
This study purpose to genetically engineer local cayenne pepper through breeding with multigamma irradiation methods to obtain superior offspring that adapt to drought stress, extreme weather, pest tolerance, and high production. The method used consists of observation, sampling, irradiation, careful selection, purification, comparative, and interpretation. The brief procedure of the study included: observations for taking samples, inventorying the physical characteristics of local chili parent varieties, selecting research sites, cultivating the planting area, irradiating the sample at a dose of 2500 rads for 30 minutes, soaking the planting area, planting seeds, irrigation, observing the age and ability to grow seeds, doing embroidery, weeding and fertilizing, observing the condition of plants during growth, harvesting, weighing the mass of fruit per tree, analyzing several nutritional content, comparing the physical and chemical characteristics of the parent varieties and selected superior offspring, and interpreting. Result of research: Local cayenne pepper of superior selected offspring as a result of multigamma irradiation can adapt to drought conditions, extreme weather, tolerant of pests and diseases, and significantly increase production compared to the parent variety. The average production of the selected superior offspring was 11.11 t ha-1, while the parent variety was 6.54 t ha-1 with a percentage increase in the production of local cayenne pepper from the selected superior offspring was 41.13%.
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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 | 0.001 | 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 | 0.001 |
| 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".