Comparisons of Two‐Row and Six‐Row Barley for Chemical Composition Using Doubled‐Haploid Lines
Bibliographic record
Abstract
Comparative studies on chemical composition between two‐row and six‐row barley (Hordeum vulgare L.) and between purple and yellow barley are very limited. Therefore, a study was conducted to compare two‐row and six‐row barley and to compare purple and yellow barley for five chemical traits. In addition, the effects of four other marker loci—srh (short rachilla hair), Raw1 (rough awn), Est1 (esterase 1), and Est5 (esterase 5)—on the five traits were also studied. One hundred ninety doubled‐haploid (DH) lines were derived from a ‘Leger’/‘CI9831’ cross by the bulbosum method. The DH lines and the two parents were evaluated for protein, starch, β‐glucan, neutral‐detergent fiber (NDF), and acid‐detergent fiber (ADF) content at two locations in Eastern Canada in 1993. Results showed that two‐row (vrs1.t) lines contained 14 to 20% more protein, 4% less starch, and 6 to 7% more β‐glucan than six‐row lines; while purple lemma (Pre2) lines contained 2 to 4% less NDF and 0 to 3% less ADF than yellow lemma lines. Differences in grain protein, starch, and β‐glucan content were associated with the Pre2 locus, but they were shown to be caused by linkage between the Pre2 and vrs1 loci. Alleles at the srh, Raw1, Est1, and Est5 loci had very little effect on the five traits. Protein content was not correlated with β‐glucan content for either two‐row or six‐row lines. Protein and β‐glucan content, however, were correlated with NDF and ADF content for the two‐row lines. Additive × additive epistasis was detected for starch and NDF content. The results of this study suggested that selection for high protein or low β‐glucan is possible in two‐row/six‐row crosses.
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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 | 0.000 |
| 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) | 0.002 | 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".