Quality Index of Passion Fruit Seedlings by Using Physically Parameters
Bibliographic record
Abstract
Passion fruit (Passiflora edulis) has aroused interest from producers, leading to an intense demand for technical information, especially for obtaining quality seedlings. The objective of this study was to evaluate passion fruit seedlings according to age and morphological characteristics. The experiment was carried out at the seedling nursery of the State University of Montes Claros, Campus Janaúba-MG, Brazil, from March to June 2017. The cultivars BRS Gigante Amarelo, BRS Rubi do Cerrado, BRS Pérola do Cerrado and Redondo Amarelo were evaluated, distributed in randomized blocks with five replications, in a split-plot scheme (4 × 4). There was an adjustment of the model (IQM = 6.3857 − 0.3892 NL + 3.3512 SD − 0.2063 SPAD + 0.0730 LA), which proposes a quality parameter of passion fruit seedlings, high level of significance and coefficient of determination, necessary for the reliability and accuracy of the results obtained. Considering the proposed model (IQM), there is no need for destructive analysis, and evaluations can be performed in the nursery itself as soon as a seedling lot reaches the recommended height of 30 cm. The evaluated characteristics contribute significantly to the quality of the seedling, and it is recommended, besides the height measurement, to evaluate the number of leaves, the stem diameter, the leaf area and the SPAD index, because the combination of these parameters will guarantee the necessary quality of the seedlings to be transplanted in the field.
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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".