Wild and cultivated allele effects on rice phenotypic traits in reciprocal backcross populations between <i>Oryza rufipogon</i> and two cultivars, <i>O. sativa</i> Nipponbare and IR36
Bibliographic record
Abstract
A total of four populations of reciprocal backcross recombinant inbred lines were produced from a cross between a wild accession of Oryza rufipogon W630 and two major cultivars, O. sativa Japonica Nipponbare and Indica IR36. Using these populations, quantitative trait locus (QTL) analysis for eight morphological traits (culm length, panicle length, days to heading, panicle shape, pericarp color, hull color, seed shattering and seed awning) was carried out, and the putative QTL regions were compared among the populations. The QTLs with strong allele effects were commonly detected for culm length, panicle shape, pericarp color and hull color in all four populations, and their peak locations were close to the major genes of sd1, Spr3, Rc and Bh4, respectively. For panicle length and days to heading, some QTL regions overlapped between two or three populations. In the case of seed shattering and seed awning, strong wild allele effects at major loci were observed only in the populations with cultivated backgrounds. Since the wild and cultivated alleles have never been evaluated in the reciprocal genetic backgrounds, the present results provide new information on gene effects in breeding and domestication studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".