Production of Flowers of Ornamental Sunflower Irrigated With Wastewater From Fish Culture
Bibliographic record
Abstract
The unregulated use and increased demand in face of the demographic and economic growth of society caused reduction in the availability of water resources in quantity and quality. In this context, this work was carried out to verify the possibility of producing ornamental sunflower (Helianthus annuus L.) fertirrigated with wastewater from fish culture and its mixtures with tap water. Two experiments were conducted in 2017 in a greenhouse at Federal Rural do University of the Semiarid, in Mossoró, RN, Brazil, according to a completely randomized design with 12 replicates. The treatments consisted of dilutions of the fish's wastewater in tap water (0, 25, 50, 75 and 100%). Determinations of plant biomass and flower characteristics were performed. The statistical analysis of the data consisted of analysis of variance by the F test and regression analysis. The biomass of ornamental sunflower showed significant effect of the fish’s effluent dilutions, but, in general, characteristics of flowers were not influenced. The higher values of the variables of the ornamental sunflower evaluated in the first experiment, which received fertilization, indicate that the effluent did not supply the nutritional needs of the plants. The irrigation of the ornamental sunflower can be carried out with effluent from fish farming when there is another source of nutrients for the crop to produce flowers with satisfactory characteristics.
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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) | 0.001 | 0.001 |
| 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".