Genetic Divergence Among Safflower Genotypes (Carthamus tinctorius L.) by Multivariate Analyzes
Bibliographic record
Abstract
Carthamus tinctorius L. is an oil seed, used both for human consumption and for industrial purposes. It is a crop that presents wide adaptability to various ecophysiological conditions, although it presents great productive potential and wide adaptability, it is still necessary to obtain technical information regarding its cultivation and of cultivars adapted and improved. In this sense, the estimation of genetic divergence using multivariate techniques has become a common tool among breeders. In view of the above, this research aimed to evaluate the genetic divergence of safflower genotypes originate from the Germplasm Active Bank (BAG) of the Instituto Mato-grossense do Algodão (IMA-MT) by means of multivariate analysis, aiming at the extension of information of this culture. The genetic divergence was estimated using multivariate analysis based on the Euclidean average distance, using the clustering optimization methods of Tocher and Hierarchical “UPGMA”. The results obtained allowed to identify the existence of genetic divergence among the evaluated genotypes, highlighting genotypes 5 and 38, which presented greater genetic divergence, constituting in potential sources of interest for the use in program of genetic improvement that aim at the development of superior cultivars of safflower.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".