Studies on Grain Yield, Physico-Chemical and Cooking Characters of Elite Rice Varieties (Oryza sativa L.) in Eastern India
Bibliographic record
Abstract
Forty one rice varieties of different ecologies are evaluated at Central Rice Research Institute, Cuttack to find out varieties having better quality characters and yield. It is revealed that hulling percentage is very good in all the genotypes and ranged from 71.0 (Vanaprava) to 81.0 (Ajay). The milling recovery varied from 62.0 (Vanaprava) to 76.0 (Radhi). The HRR% varied from 43.5 (Kalyani-2) to 68.0 (Pooja). The kernel length is highest in Geetanjali (7.54) and lowest in Nuadhusara (3.88). Kernel length after cooking is very important character and varied from 7.9 (Nuakalajeera) to 12.5 (Geetanjali). Elongation ratio is highest in Nuadhusara (2.07) and Nuakalajeera (2.0) and lowest in Chandan (1.44). Volume expansion ratio is highest in Nua kalajeera (5.25), Nuadhusara (5.15) and lowest in Udaya (3.25). Amylose content is intermediate in all the tested genotypes except Heera and Vanaprava and it ranged from 22.1 (Utkalprava) to 26.1 (Vanaprava). The yield is highest in Rajlaxmi (7100 kg ha–1) followed by Ajay (6400 kg ha–1), Satyakrishna (6300 kg ha–1), Vashadhan (6200 kg ha–1) and lowest in Kalinga-3 (3100 kg ha–1).
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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 | 0.001 |
| 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".