Genetic Improvement of Local Red Peanut With Using Nuclear Technique (Multigamma Irradiation) for Obtaining Superior Variety
Bibliographic record
Abstract
The main problem examined in this study concerns the breeding of local red peanuts (Arachis hypogaea L.) to use standard multigamma irradiation methods to obtain superior variety that can be adapted to drought conditions, pests-diseases, extreme climate, and high production. The research objective was to develop local red peanut variety to use multigamma irradiation and careful selection for obtaining superior variety that can be adapted to drought conditions, pests-diseases, extreme climate, and high production. Research methods include multigamma irradiation, observation, sampling, carefully selection, comparative, and interpretation. The results of the study are as follows: The development of local red peanut variety through breeding to use multigamma irradiation and careful selection resulted in superior variety that could adapt to drought conditions, pests-diseases, extreme climate, and increased production significantly. The range of production of selected superior variety resulting from multigamma irradiation was (3.68-4.10) t ha-1 with an average production of 3.92 t ha-1. The percentage increase in the production of selected superior variety was 49.23% with an average water content of dry seeds of 11.3%.
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 | 0.001 |
| 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".