Genetic Divergence Among Gerbera spp. Genotypes Based on Morphological Traits
Bibliographic record
Abstract
Gerbera is one of the most important ornamental flowers marketed worldwide. Information on the genetic variability of this species represents an important resource for improvement programs that rely on the selection of promising genotypes. The aim of the present study was to assess and characterize the genetic divergence among 32 gerbera accessions based on morphological traits by means of multivariate analysis. A total of 21 traits, nine quantitative and 12 qualitative, were analyzed using clustering analysis and the principal component method. Although the quantitative and qualitative traits were analyzed separately, joint analysis allowed for a more reliable interpretation of the genetic variability and also permitted the identification of wide dissimilarity among the investigated accessions. With regard to the quantitative traits, four main groups were observed, which was in partial agreement with the result of the analysis of canonical variables. The qualitative trait analysis, on the other hand, resulted in the formation of five groups. The traits that contributed most significantly to variability in gerbera were total width of the trans florets set and number of flowers. The data obtained in the present study may be applied directly to improve the design of controlled crosses between gerbera accessions and to better explore the hybrid effect in the process of genetic improvement in this species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".