Growth of Okra Under Nitrogen Rates and Wastewater in the Brazilian Semiarid Region
Bibliographic record
Abstract
Water is one of the most important natural resources, especially for semiarid regions where it is very limited. Thus, some alternatives to preserve water are necessary. In this sense, we aimed to evaluate the effect of irrigation with post-treated domestic wastewater associated with different nitrogen rates on the growth of okra in the semiarid region of Brazil. The experiment was performed in the municipality of Pombal, state of Paraíba, Brazil. It was used a randomized block design with six nitrogen rates (N1 = 0, N2 = 40, N3 = 80, N4 = 120, N5 = 160, and N6 = 200 kg ha-1) and wastewater corresponding respectively to 0; 25; 50; 75; 100; and 125% of the fertilization recommendation for the okra crop. In addition to these treatments, a control was added and the plants received 100% of the recommended dose of nitrogen and they were irrigated with water (IW). The control was compared with the treatments that were irrigated with wastewater and received the minimum (0%) and the recommended (100%) doses of nitrogen fertilization. The use of treated wastewater is an excellent technique for the reuse of water in semiarid regions, but it does not fully meet the okra nitrogen requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".