Inheritance of stem strength and its correlations with culm morphological traits in wheat (<i>Triticum aestivum</i>L.)
Bibliographic record
Abstract
Yao, J., Ma, H., Zhang, P., Ren, L., Yang, X., Yao, G., Zhang, P. and Zhou, M. 2011. Inheritance of stem strength and its correlations with culm morphological traits in wheat ( Triticum aestivum L.). Can. J. Plant Sci. 91: 1065–1070. The genetic effect of stem strength and its correlation with culm morphological traits were investigated in a 7×7 diallel cross of wheat involving seven parents (Ningmai 8, Ningmai 9, Yangmai 5, Yangmai 9, Yangmai 11, Sumai 3, and Wangshuibai) during the crop season of 2009–2010. Significant differences were observed among genotypes for stem strength. The estimates of general combining ability (GCA) pointed out that the best general combiners for stem strength were Ningmai 8 and Yangmai 9. The additive-dominance model was adequate for stem strength, and it was controlled by the over dominance type of gene action. Ningmai 8, followed by Yangmai 5, possessed maximum recessive genes, whereas Wangshuibai had maximum dominant genes. Stem strength could be controlled by three genes with low narrow sense heritability. A statistical analysis showed that stem strength is highly significantly correlated with nine culm morphological traits except for diameter of the first basal internode.
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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 | 0.001 |
| 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".