Growth Rate of Eggplant Under Nitrogen and Phosphate Fertilization and Irrigated With Wastewater
Bibliographic record
Abstract
Wastewater use has become an alternative for agriculture in arid and semi-arid areas due to water scarcity besides providing nutrient for plants. This work aimed to evaluate the effects of the interaction of nitrogen and phosphorus doses associated with wastewater on the growth rate of eggplants in the stages of vegetative growth and beginning of fruiting in the semi-arid of Brazil. The wastewater was previously treated using sand filter with intermittent flow. The experiment consisted of a randomized block design in a 4 × 4 + 1 factorial scheme, with four replications. The factors consisted of four nitrogen doses (N1 = 0.22, N2 = 0.39, N3 = 0.56 and N4 = 0.72 g dm-3 of soil), four doses of phosphorus (P1 = 0, P2 = 1.68, P3 = 2.40 and P4 = 3.12 g dm-3 of soil), both using wastewater, and a control treatment (100% nitrogen and phosphorus using drinking water). The interaction of wastewater with nitrogen and phosphorus doses influenced all growth rates at the vegetative stage, except for the relative number of leaves. We found interaction for the relative rates of stem diameter and number and area of leaves at the beginning of the fruiting stage. Nitrogen and phosphorus doses associated with wastewater were excessive even below the recommendation for eggplant cultivation, however, in the absence of wastewater the plants reduce the growth rates of stem diameter and leaf area in the initial period of the fruiting.
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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".