Determination of Microbiological Quality and Chlorophyll Levels Lettuce Grown Hydroponically With Wastewater
Bibliographic record
Abstract
The study aimed to determine the microbiological quality for coliform in three lettuce cultivars grown hydroponically with optimized solutions. The study was conducted in a protected environment and driven in a hydroponic system adopting the technique of laminar flow nutrientes-NFT, on the premises of the State University of Paraíba-Campus II in Lagoa Seca, PB having the following coordinates: 7°10′15″S; 35°51′14″W. The experimental design was a randomized block with a split-plot treatment in 7 × 3 factorial, with three replicates whose factors were 7 hydroponic solutions with three lettuce cultivars. The optimized solutions were formulated taking as reference the nutrient solution Furlani. The plot was constituted by nutrient solution (S). S1 = Furlani solution; S2 = domestic wastewater; S3 = optimized domestic wastewater; S4 = well water; S5 = optimized well water; S6 = wastewater from the UASB reactor and S7 = optimized wastewater from the UASB reactor and as for the subplot the three lettuce cultivars (Thais, Vanda and Veronica). In none of the samples of the three lettuce cultivars in the first experiment did the microbiological quality increase at 45°C, by the Most Probable Number (MPN) table, when the tube combination was 0-0-0, that is, without growth in none of the tubes, it is assumed that the MPN is less than 1.8 μg. ll samples were considered acceptable for the microbiological standard of thermotolerant coliforms at 45 °C/g. The leaf chlorophyll content is positively correlated with the N content in the plant.
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 | 0.000 |
| 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".