Quality of Yellow Bell Pepper Fruits Cultivated in Fertilized Soil with Yellow Water and Cassava Wastewater
Bibliographic record
Abstract
Currently there is a great need for reuse of water in agricultural activity, aiming at reducing environmental impacts and production costs. The objective of this study was to evaluate the fruit production of hybrid Satrapo bell pepper, under fertilization with yellow water and cassava wastewater. The experiment was conducted in a greenhouse located at Campina Grande city, PB. The experimental design was completely randomized, with eight treatments and five replications, totaling 40 experimental plots. The treatments were characterized by fertilization with cattle manure (EB); NPK; human urine (HU); cassava wastewater (M); cassava wastewater and human urine (UH+M); the double volume of human urine (2xUH); the double volume of cassava wastewater (2xM); and the double volume of human urine and cassava wastewater (2xUH+M). At 60 day after transplanting (DAT) were evaluated the diameter, thickness of mesocarp, fresh and dry phytomass and number of lobes of yellow bell pepper fruits.According to the results, there were significant differences in the variables analyzed due to the treatments applied. The treatment corresponding to 2xM provided the highest results for length, diameter, thickness of the mesocarp, fresh and dry phytomass of the bell pepper fruits. On the contrary, 2xUH was the treatment that provided the lowest values for these variables and provided the highest number of lobes.
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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".