Genetic Divergence in Urena lobata Accessions to Quantitative Traits
Bibliographic record
Abstract
In this study, 14 quantitative traits were evaluated, divided into five distinct categories, related to the plants, leaves, flowers, fruits and seeds of seven Mallow accessions belonging to the NUSEC/UFAM germplasm collection. With the characteristics measured, the genetic variability of the accessions was evaluated, aiming at the genetic improvement of the culture. Through the analysis of variance, it was possible to assemble and propose the 14 agronomic descriptors, being that with the application of the clustering method (UPGMA), which generated a dendrogram of dissimilarity, it was possible to divide the seven genotypes into two distinct phenotypic groups, and their subgroups. The study showed that there is a great divergence between the accesses and the recommendation for the crossover is between the accesses ANÃ/MAC, ANÃ/MAQ and PIN/BER. The high heritability observed for the characters analyzed suggests the phenotypic value as a reliable measure for selection of the genotypes studied.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".