Quality Difference of Near-Isogenic Lines with HMW Glutenin Subunits 7+8~* and 17+18 in Wheat Cultivars Longmai 20
Bibliographic record
Abstract
【Objective】 Determining the genetic differences between high molecular weigh glutenin subunits (HMW-GS) 17+18 and 7+8*.【Method】 HMW-GS 7+8* was introduced into Longmai 20 (1,17+18, 5+10) by 5 consecutive backcrosses with biochemical marker assisted selection from a Canadian extra strong wheat cultivar Glenlea possessing an over expression of the Bx7 subunit. The near isogenic lines (NILs) of HMW-GS 17+18 and 7+8* were obtained and grown in the experimental field of Crop Breeding Institute of Heilongjiang Academy of Agricultural Sciences, Harbin in 2005. The experimental design was a randomized block with six replicates. 【Result】In comparison to NILs with 17+18 subunit, the average of quality parameters of NILs with 7+8* subunit were 5% (P=0.012), 4% (P =0.018), 10% (P=0.013), 13% (P=0.258), 256% (P=0.029) and 86% (P =0.020) higher in flour protein content, dry gluten content, Zeleny sedimentation, development time, stability and breakdown time, respectively, and were 2% (P =0.043) and 35% (P=0.007) lower in the ratios of wet gluten content to dry gluten content (wet gluten/dry gluten ) and degree of softening, respectively. 【Conclusion】The subunits 7+8* have a strong positive effect on dough strength.
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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 | 0.000 | 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) | 0.001 | 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".