Mineral Composition of Lettuce Grown in Hydroponic System With Wastewater
Bibliographic record
Abstract
The use of treated effluents is not a new practice in agriculture, however, the optimization of wastewater was given with mineral fertilizers to grow lettuce in a hydroponic system, subject that is still barely studied. The objective of this study was to evaluate the mineral composition of three lettuce cultivars (Verônica, Vanda and Thais) in a hydroponic system using wastewater, well water and optimized nutrient solutions. The plants was grown in seven nutrient solutions, as S1 = Furlani solution; S2 = domestic wastewater; S3 = optimized domestic wastewater; S4 = well water; S5 = optimized well water; S6 = wastewater UASB and S7 = optimized UASB wastewater and the sub-plot for three lettuce cultivars. It was verified that the treatments S2, S4 and S6 when compared with the respective optimized solutions S3, S5 and S7 presented lower levels of nitrogen, potassium, calcium, zinc, copper and manganese in the two experiments. Same behavior was not observed for phosphorus and sodium. As for the cultivars, they presented, regardless of the experiment, mineral composition similar to each other when the same nutrient solution was used.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Agronomy experiment on the mineral composition of hydroponic lettuce grown with wastewater.
It studies mineral composition in hydroponically grown lettuce, not research practice.
Agricultural hydroponics study of lettuce mineral composition with wastewater.
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 | 0.001 |
| 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".