Quantitative trait loci for root-penetration ability and root thickness in rice: Comparison of genetic backgrounds
Bibliographic record
Abstract
Drought is the major abiotic stress limiting rice (Oryza sativa) production and yield stability in rainfed lowland and upland ecosystems. Root systems play an important role in drought resistance. Incorporation of root selection criteria in drought resistance improvement is difficult due to lack of reliable and efficient screening techniques. Using a wax-petrolatum layer system simulated to compacted soil layers, root traits were evaluated in a doubled haploid (DH) population derived from the cross between 'IR64' and 'Azucena'. Twelve putative QTLs (quantitative trait loci) were detected by interval mapping comprising four QTLs for root-penetration ability, four QTLs for root thickness, two QTLs for penetrated root number, and two QTLs for total root number. These QTLs individually explained 8.4% to 16.4% of the phenotypic variation. No QTL was detected for maximum penetrated root length by interval mapping. One QTL located between RG104 and RG348 was found to influence both root-penetration ability and root thickness. QTLs for root-penetration ability and root thickness were compared across two populations, 'IR64'-'Azucena' and 'CO39'-'Moroberekan', and different testing conditions. The identified consistent QTLs could be used for marker-assisted selection for deep and thick roots with high root-penetration ability in rice. Key words: quantitative trait loci, root-penetration ability, root thickness, rice, drought.
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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 | 0.000 |
| 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".