Heritability and Combining Ability Studies in Strawberry Population
Bibliographic record
Abstract
The most efficient breeding strategies in crop improvement is the selection based on heritability and combing ability estimates for the traits of economic importance or commercial value. Therefore, the present study was to obtain estimates of heritability and to estimate the phenotypic and genotypic correlations among the characteristics of interest. The commercial cultivars ‘Aromas’, ‘Camarosa’, ‘Dover’, ‘Festival Flórida’, ‘Oso Grande’, ‘Sweet Charlie’ and ‘Milsei-Tudla’, and 103 F1 hybrids from the crossbreeding experiments were evaluated for four traits of commercial fruit yield and 13 traits of fruit physical and chemical quality. The estimated genetic parameters were general combining ability, specific combining ability, genotypic correlation among traits, estimates of heritability, genetic and phenotypic variance. The ‘Camarosa’ and ‘Aromas’ cultivars were the most promising cultivars for use as parents in the commercial fruit production, while ‘Dover’ and ‘Sweet Charlie’ cultivars were selected for taste of fruit in strawberry breeding, as they showed higher concentrations of favorable alleles in the F1 hybrid population. It was also verified some strong genetic correlations for some pairs of characteristics in the present study that may allow indirect selection. The estimation of these parameters is an important basis for decision making on the genetic engineering of strawberry.
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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) | 0.000 | 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".