Effect of the Substrate and Containers in the Initial Growth of Seedlings of Physalis peruviana L.
Bibliographic record
Abstract
Physalis peruviana L., from the family Solanaceae, is a fruitful species of high nutritional and economic value, incorporated in the category of the small fine fruits in Brazil, and is a promising source of income for small farmers, mainly in the Northeast area. This work aims to evaluate the influence of different substrate and containers in the initial growth of Physalis peruviana L. The experiment was conducted in the greenhouse, in a completely randomized designing. Two types of containers (polypropylene seedling tray of 200 cells and containers of polypropylene of 50 mL) and three compositions of the substrate (commercial substrate Hortiplant®, and ravine soil + sand + organic compost in the proportions 2:1:1 and 2:1:2) were tested. The analyzed variables were: percentage of emergency and emergency velocity index of the plants; height of the plants; the number of leaves; length of the main root; and wet and dry mass of the root and the aerial part at the 30 days after sowing. Analyses of variance were used to test the effects of substrate and containers on the studied variables, and the averages of the studied variables were compared among treatments using the Tukey’s test at 5% of probability. In the greenhouse conditions, Physalis peruviana L. seedlings grow better when sowed in the commercial substrate using the container of polypropylene of 50 mL, resulting in larger seedlings, a larger number of leaves and a proper development of the root system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".