Cluster analysis in rapeseed (Brassica napus L.)
Bibliographic record
Abstract
Many oilseed crops (e.g. sunflower, soybeans, rapeseed/mustard, sesame, groundnuts etc) are grown in Kenya. But oilseed rape is preferred because of its high yields (1.5 tons – 4.0 tons/ha) with high oil content of 42 – 46%. It is soft seeded hence oil extraction is relatively easy. The meal is high in protein and very useful in livestock feed supplementation. The success of any crop improvement programme depends on the extent of genetic diversity in the material. Hence, it is essential to evaluate introductions for adaptation and study the similarities if any among them. Evaluation trials were carried out on 17 rapeseed genotypes (nine of Canadian origin and eight of European origin) at 4 locations in Kenya namely Endebess, Njoro, Timau and Mau Narok for two years. An analysis of variance was carried out on seed yields which indicated that the genotypes were significantly different (LSD, 0.05). Cluster analysis based on mean seed yields suggested only one major group existed within the material. In the first year, genotypes 2, 3, 8 and 9 didn’t group with the rest. Genotype 8 was the only one that did not classify with the rest of the Canadian genotypes. Three European genotypes (2, 3 and 9) were however not classified with the others. In the second year, genotypes 10 and 6 didn’t fall in the major cluster. Of these two, genotype 10 is of Canadian origin. Genotypes were more similar in the second year than the first year due to favorable weather. It is evident that genotypes from different geographical areas, that is, Europe and Canada fell in the same clusters suggesting that they have genetic similarity.The groupings indicated no correspondence between geographical diversity and clustering pattern. Key words: Genetic diversity, cluster analysis, genetic similarity and cluster pattern.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".