Principal Component Analysis for Identification of Superior Castor Bean Hybrids
Bibliographic record
Abstract
The identification of superior genotypes in plant breeding programs is not a quick and simple task and requires breeders to become aware of more suitable and efficient tools for evaluating crop performance. Univariate analyses are often too narrow for the scope of plant breeding because it lacks consideration of relations between variables. Therefore, the objective of this study was to select castor bean hybrids based on principal component analysis (PCA). Trials were conducted in 2017 with 31 hybrids in a randomized block design with 4 replications. The following variables were used to evaluate crop performance: plant height (PH), insertion height of the primary raceme (HPR), number of stem nodes (NN), number of racemes (NR), number of seeds (NS), stem diameter (SD), number of fruits (NF), 100-seed weight (S100) and seed oil content (SOC). The first three principal components (PCs) explained approximately 75.01 % of all the variability in the dataset. PC 1, 2 and 3 were particularly related to productivity (NS, NR, S100 and NF), plant size (SD, HPR and PH) and oil production (SOC), respectively. Hybrids 14 and 23 were the most suitable for grain production in commercial scale due to short-height, which favors mechanical harvesting. Commercial hybrid 26 showed high SOC, medium grain yield and medium-height.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".