Growth and Nutritional Status of Passiflora edulis f. Flavicarpa, With the Application of Organic Compound in Amazonian Soil
Bibliographic record
Abstract
Growth with organic fertilizers has increased in recent years because of the beneficial effect of organic matter on intensely cultivated soils and the high costs of mineral fertilizers. In order to evaluate the effects of organic compost doses produced from family farming waste on the growth and nutritional status of the passion fruit, an experiment was carried out in a greenhouse at the Universidade Federal Rural da Amazônia, in Belém, State of Pará, in the period from March to June, 2012. The experimental design was completely randomized, with five treatments and four replications, with each experimental plot made up by a pot with a volume of 3.6 dm3 of soil and a yellow passion fruit seedling. Five doses of organic compost (0%, 15%, 30%, 45%, and 60%) were tested out of the total volume of the substrate. The compost was formed by mixing 10% poultry litter, 20% duck litter, 15% manioc husk, 15% cassava leaf, 15% bean straw, 15% rice husk, and 10% corn cob. The different amounts of organic compost were mixed in volumetric proportions of substrate of Yellow Latosol with a sandy texture, taken from the surface layer (0-20 cm). It was found that at 97 days, the best results were achieved at the dose of 60% of the compost. The content and accumulation of macronutrients in the foliar tissue of the yellow passion fruit plants followed this descending order: K > N > Ca > P > Mg ≥ S.
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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 | 0.000 | 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".