An Insight into the Glycemic Index of Rice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it