Studies on Grain and Food Quality Traits of Some Indigenous Rice Cultivars of North-eastern Hill Region of India
Bibliographic record
Abstract
Searching rice cultivars or variety with good processing and high in important essential nutrients are prime important in the present context of rice research. The north eastern hill region of India which is a mega biodiversity hot spot of the world has numerous cultivars of rice with tremendous potential of high quality rice. Eighteen indigenous cultivars of Tripura, a north eastern hill state of India were subjected to the study. Majority of the cultivars were of short bold grain type. Eleven cultivars were aromatic type with one cultivar of strong aroma comparable to Basmati rice. Eleven cultivars were found to possess higher hulling percentage more than 65% and six cultivars with more than 65% out turn. Majority of the cultivars had low amylose contents (<20%). Four cultivars were having higher total crude protein (>10%), six cultivars were having higher iron contents (>10ppm). In most of the characters, heritability (h2) was more than the genetic advance indicating more of environmental effect. Amylose, crude fibre and iron, genetic advance was high; selection for these traits will improve the genotypic value of selected plants over the parents. High heritability coupled with high genetic advance was found in iron and amylose. The association of crude protein is significant but negative with carbohydrates and amylose, while with crude fat and zinc, the association is significant and positive. Carbohydrate was significantly and positively correlated with ash percent and iron concentration and negatively associated with total fat. There was no significant correlation between carbohydrate and amylose.
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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 | 0.001 |
| 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".