Variations in Grain Traits among Local Rice Varieties Collected More Than Half-Century Ago in Indochinese Countries
Bibliographic record
Abstract
More than half-century ago, local rice varieties were collected from Indochinese countries (Cambodia, Thailand, Laos, and Vietnam). Of these, 162 local varieties were examined for 7 grain-size traits: seed length/width/thickness, brown rice length/width/thickness, and 100-seed weight. Since these traits varied considerably, a survey of functional mutations was performed in the genes related to these traits. In total, 19 markers (12 InDel and 7 dCAPS markers) were used to investigate the mutations at 14 grain-size loci of GW2, GS2, qLGY3, GS3, GL3.1, TGW3, GS5, GW5, GS6, TGW6, GW6a, GLW7, GL7, and GW8. Significant allele effects were observed with six markers detecting base substitution mutations at GW2 and GS3 and insertion/deletion mutations at GS5, GW5, and GW6a, suggesting that these mutations might have affected the grain trait and caused variation among local varieties in the Indochinese countries. In addition to grain size, the hull color, grain color, and glutinosity were also examined using a survey of loss-of-function mutations at major responsible loci. Most phenotypes were reflected based on functional mutations at these loci. Since the local varieties have wide genetic variation, they are important genetic resources for future rice breeding.
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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 | 0.001 | 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".