Agronomic Performance of Passion Fruit Genotypes in the Federal District, Brazil
Bibliographic record
Abstract
Brazil stands out as the largest producer and consumer of passion fruit in the world. However, this fruit specie still faces some production problems such as lack of genetic materials with high yield, disease resistance and fruit quality, due mainly to the lack of research work in the breeding area. In order to contribute to the development of new passion fruit cultivars, this study aimed to evaluate the agronomic performance of passion fruit genotypes in the Federal District, Brazil, as well as to estimate genetic parameters for use in breeding programs. The experiment was carried out with 48 genotypes, in a simple layout (arrangement) of randomized block, with four replications and six plants per plot. The following characteristics were evaluated during fifty-four crops: fruit yield (kg/ha), number of fruit per hectare, average fruit weight (g) and sorting fruit on the equatorial diameter (mm) in five categories (1st, 1B, 1A, 2A and 3A). Higher fruit yields and number were observed in the genotypes MAR 20 # 41, MAR 20#41 pl 1, Gigante Amarelo pl 1 and MAR 20 # 39. Consdering fruits of 1st, genotype MAR 20 # 39 pl 2 produced the highest number of fruits per hectare. High values of heritability and CVg/Cve ratio were observed for total number of fruits per hectare in the first classification.
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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".