Effect of Gamma Irradiation on Morpho-Agronomic Characteristics of Groundnut (<i>Arachis hypogaea</i> L.)
Bibliographic record
Abstract
Induced mutation in plant improvement has been used in several crops to generate new sources of genetic variations. A study was conducted to determine the effect of different doses of gamma irradiation on different morpho-agronomic characteristics. Agronomic traits that were analyzed included: grain yield, number of pods/plant, number of seeds/plant and weight of 100 seeds and numbers of days to 50% flowering. Morphometric characterisation of the descriptive data included plant height, stem diameter, number of leaves/plant, leaflet length, leaflet width and number of ramification/ plant. Groundnut seeds were treated with various doses of gamma rays (100, 200, 400 and 600 Gy). Among the various dose treatments, gamma rays treatment at 100 Gy resulted in a higher increase of grain yield and other morpho-agronomic parameters especially for the JL24 variety. In fact the gamma irradiation at 100 Gy increased significantly grain yield by 14% for JL24, and 4 % for JL12. The number of pods per plant was increased by 2% for JL12 and 37% for JL24. For the number of seeds per plant, there was a significant increase of 8% for JL12, and 62% for JL24 at 100 Gy. A similar trend was observed for the JL24 at 200 Gy dose. Higher doses of gamma rays (400 and 600 Gy) reduced significantly plant growth and grain yield. The usefulness of the mutants identified in a groundnut breeding program is discussed.
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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".