Clustering and Principal Components Analysis of Xinjiang Spring Wheat Cultivars
Bibliographic record
Abstract
For giving some message to develop new spring wheat variety in Xinjiang,14 traits of 34 varieties were analyzed by principle component,and the varieties had been clustered based on principal components factors score.There were rich variation of main traits,the coefficient variation were in the order of sterile spikelet number spiking rate total stem number kernel-mass per main spike harvested spike number yield kernel number per main spikespikelet number1000-kernel mass spike length basic seedlings spikelet number per spikeplant height growth duration.The yield of 61.76% varieties were more than 5 250 kg/hm2,and Xinchun 20,Xinchun 23,and Xinchun 26 had higher yield.Principle component analysis indicated that the characteristic vector accumulated contribution rate of 7 principal components were 89.34%,followed by spike traits factor,grain formatting factor,plant height factor,population size factor,growth period factor,yield factor,and grain mass factor.According to 7 principal components factors score,34 varieties could be divided into 2 categories.Group Ⅰ was representative varieties group with higher population size and yield,group Ⅱ was representative varieties group with lower population size and yield.Main characters of Xinjiang spring wheat have rich variation,and yield traits have wider range of selection option.Growth period is one of the important traits,and population size is important factor for deciding yield.Xinchun 2,Xinchun 17,Xinchun 19,Xinchun 20,and Ningchun 33 have better comprehensive performance in 14 traits.
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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) | 0.002 | 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 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".