An Insight into the Glycemic Index of Rice
Bibliographic record
Abstract
Glycemic index of rice is a highly complex trait. Rice varieties possessing slowly digestible starch (high amylose) are potentially characterized to have low glycemic index and can be useful for management of type II diabetes. Understanding genetic mechanisms underlying starch biosynthesis and metabolism of cooked rice can pave the way for developing efficient breeding and selection strategy for combining high grain yield with low glycemic index. In this context, reverse genetics can prove useful. Available rice genome sequence information encoding key enzymes involved in biosynthesis of amylose component of starch can unravel novel alleles involving single nucleotide polymorphisms (SNPs). A multi-allelic waxygene (Wx) encoding Granule-Bound Starch Synthase I (GBSS I) enzyme is known to determine amylose content in rice endosperm. Potential molecular markers are now available to detect GBSS I alleles (SNPs) associated with five classes of amylose (waxy: 0–5%, very low: 5–12%, low: 12–20%, intermediate: 20–25%, and high:25–33%). These can be routinely used to assist breeding programme. Besides, the presence of intra-class variations in amylose content could be attributed to additional regulatory elements or environmental conditions.
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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 | 0.001 | 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".