Growth of Fertilized Maize via Fertirrigation and Foundation With Treated Human Urine and Cassava Wastewater
Bibliographic record
Abstract
The agricultural reuse of wastewater is already a reality in several countries of the world. The objective of this work was to evaluate the growth of hybrid corn AG 1051 grown in fertilized soil via the foundation and fertirrigation with the combination of human urine and treated cassava and NPK in protected environment.The experiment was conducted at the Federal University of Campina Grande, Campina Grande, Paraíba. The experimental design was the completely randomized one with 14 treatments and four replications, being two experiments, one fertilized via foundation and the other via fertigation. The treatments were as follows: T1 (NPK), T2 (470 mL), T3 (670 mL), T4 (870 mL), T5 (1070 mL), T6 (1270 mL), T7 (1470 mL) applied via fertilization of fertirrigado, and the same dosages applied via fertirrigation corresponding to T9 mineral fertilization, and T10, T11, T13 and T14 the different doses of organic fertilization. There was an average increase of 17.32% in the Index of Emergency Speed for the seeds that were cultivated in soil fertilized with T3 and T4 in relation to the one fertilized with NPK (T1), the soil fertilized with T3 was the one that presented the highest %E (Emergency Percentage) with 98, 81%. At 15 days after the emergency (DAE), the chemical fertilization through the foundation promoted greater leaf area gain differently than at 65 DAE. The organic fertilization via fertigation promoted the highest gains for absolute and relative growth rates of plant height stem diameter and number of leaves.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".