Genetic Variability of Passion Fruit Multispecific Hybrids and Their Respective Wild Parents Determined by Microsatellite Markers
Bibliographic record
Abstract
The Passiflora genus comprises more than 500 species that are used for food, industrial, ornamental, and pharmaceutical purposes. The sour passion fruit (P. edulis Sims) has low genetic variability for disease resistance, and the use of wild species in the cross-breeding basis is a promising alternative for introgression of resistance genes. The objective of this study was to characterize multispecific hybrids and wild materials with potential to be used as parents in passion fruit genetic breeding programs, using microsatellite markers. Genomic DNA from 33 accessions was extracted and analyzed using 23 microsatellite markers, which were used to estimate the genetic dissimilarities among accessions. The genetic dissimilarity matrices were used to perform clustering analysis by dendrogram using the Unweighted Pair-Group Method as grouping criterion and by graphic dispersion based on multidimensional scale, using the principal coordinates method. Genetic distances between accessions ranged from 0.067 to 1.00. The markers indicated genetic variability among the studied accessions and also the efficiency of the recurrent genome recovery within the backcross program. The genetic structure among the accessions shows the clustering tendency between the wild accessions of P. hatschbachii and P. quadrifaria and the accessions obtained by crossing these species. The same occurred for P. incarnata and P. edulis accessions. The knowledge generated by the molecular characterization provides information on the diversity of accessions and contributes to the work of breeders in the selection of parents.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".