Interrelationships Among some Morphological Traits of Wheat (Triticum Aestivum L.) Cultivars using Biplot
Bibliographic record
Abstract
Abstract Sabaghnia N., Janmohammadi M., 2014: Interrelationships among some morphological traits of wheat (Triticum aestivum L.) cultivars using biplot [Kviečių (Triticum aestivum L.) veislių morfologinių požymių sąveika naudojant biplot metodą]. - Bot. Lith., 20(1): 19-26. Wheat (Triticum aestivum L.) is one of the major food crops worldwide and Iran produces about 14 million tons of wheat annually. Effective interpretation of the data on breeding programmes is important at all stages of plant improvement. The cultivar by trait (CT) biplot was used for two-way wheat dataset as cultivars with multiple traits. For this propose, 13 wheat cultivars with specific characteristics were tested and the CT biplot for wheat dataset explained 65% of the total variation of the standardized data. The polygon view of CT presented for 18 different traits of wheat cultivars showed six vertex cultivars as G3, G4, G5, G9, G11 and G12. The cultivar G4 had the highest values for most of the measured traits. Generally based on vector view, ideal cultivar and ideal tester biplots, it was demonstrated that the selection of high grain yield will be performed via thousand seed weight, spike length and grain diameter. These traits should be considered simultaneously as effective selection criteria evolving high yielding wheat cultivars because of their large contribution to grain yield. The cultivars G3 and G4 could be considered for the developing of desirable progenies in the selection strategy of wheat improvement programmes
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".