Mapping quantitative trait loci for water uptake in a recombinant inbred line population of natto soybean
Bibliographic record
Abstract
Molnar, S. J., Charette, M. and Cober, E. R. 2012. Mapping quantitative trait loci for water uptake in a recombinant inbred line population of natto soybean. Can. J. Plant Sci. 92: 257–266. Small-seeded natto soybeans are soaked in the first step of producing natto. Water uptake traits play a role in the quality of the end product. The objectives of the current study were to use a recombinant inbred line (RIL) mapping population contrasting for water uptake traits to develop its molecular marker recombination map, and to use quantitative trait locus (QTL) analysis to characterize the genetics of water uptake and identify molecular markers for marker assisted breeding. A RIL population (AC Colibri×OT91-3) was tested for multiple years at Ottawa, Ontario, Canada. Two water uptake parameters (a16 and b) were estimated by fitting a curve for an exponential rise to a maximum. Both parameters were affected by year, genotype and the interaction effects. Seed yield, seed composition and agronomic traits were also measured. Simple sequence repeat (SSR) markers were used to genotype the population and develop a recombination map. QTL analysis identified two QTL for a16 on molecular linkage groups (MLG) D2 and E and three QTL for b on A2, J and M. Three of these QTL map to similar intervals as known QTL for seed weight, seed yield and seed fill in diverse populations. The fourth may correspond with a known QTL for water absorbability during germination and the fifth maps at or near known flowering time and maturity QTL.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".