Growth and Production of Millet Irrigated With Dilutions of Treated Gray Water
Bibliographic record
Abstract
The present work aimed to analyze the effects of the application of dilutions of treated gray water (TGW) in well water (WW) on growth and production of millet cv. Ceará (Pennisetum glaucum). The experiment was carried out in a greenhouse, on the department of agronomic and forestry sciences, at the Federal Rural University of the Semi-Arid (UFERSA), Mossoró, RN, Brazil. The experimental design was randomized blocks with five treatments and six replications, totaling thirty plots. The experiment was carried out in vases with volume of 25L containing four plants per vase. The treatments consisted in five dilutions of TGW in WW: T1—100% WW plus 0% TGW; T2—75% WW plus 25% TGW; T3—50% WW plus 50% TGW; T4—25% WW plus 75% TGW and T5—0% WW plus 100% TGW. During the experiment it was analyzed the attributes of plant height, number of tillers, number of leaves, stem diameter and total fresh and dry matters. With the results, it was observed that the exclusive irrigation with TGW (T5) promoted better millet development. As the concentrations of TGW increased in dilutions, also increased plant height, number of leaves and tillers. The rise in the number of leaves is associated to the rise in height and tillers, and those are associated to the rise in stem diameter and fresh matter, resulting in better accumulations of dry matter and showing the viability of gray water use to improve forage production and increasing potable water availability to multiple uses.
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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 | 0.001 |
| 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".