Human Urine and Treated Domestic Effluent for Ornamental Sunflower Cultivation
Bibliographic record
Abstract
This study aimed to evaluate growth and production of ornamental sunflower (Helianthuns annuus L.), cv. Anão de Jardim, irrigated with dilutions of human urine in treated domestic effluent. The experiment was carried out in completely randomized design with five treatments and four replicates, in a greenhouse. Treatments consisted of four dilutions of human urine (0, 2, 4 and 6%) in treated domestic effluent and fertilization with 50% of the recommendation of mineral fertilizer. Plants irrigated with human urine dilutions (2, 4 and 6%) in treated domestic effluent and fertilized with 50% of the recommendation of nitrogen (N) and potassium (K) showed reductions of growth and production, while those irrigated with 0% human urine dilution in treated domestic effluent and fertilized with 50% of N and K recommendation exhibited similar performance to those irrigated with public-supply water and fertilized with 100% of the recommendation of mineral fertilizer. The obtained results allow to infer on the viability of replacing public-supply water by treated domestic effluent for irrigation, with reduction of 50% in N and K fertilization, without prejudice to the growth and production of ornamental sunflower plants, cv. Anão de Jardim.
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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 | 0.001 | 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".