Growth Response of Hydroponic Lactuca sativa L. to Application of Fertilizer Organic VIUSID Agro®
Bibliographic record
Abstract
Lettuce (Lactuca sativa L.) production is a valuable crop for supporting family farms in the Brazilian semiarid region. The cultivation of this vegetable in a hydroponic system with foliar application of organic fertilizer can help in mitigating the challenges imposed by the region’s edaphoclimatic conditions. Therefore, it is important to increase the lettuce production using new technologies. VIUSID Agro® is an example of such technology that has the potential to increase lettuce yields. VIUSID Agro® is a natural compound that actives molecular processes without affecting the environment adversely. This product acts as a natural bioregulator and is basically composed of amino acids, vitamins, and minerals. The experiment’s objective was to analyze the impact of VIUSID Agro® organic fertilizer on the growth of hydroponic lettuce. The research was conducted on a family farm in Bom Jesus, Paraíba state, Brazil. The experimental was a completely randomized design with 5 VIUSID VIUSID Agro® concentrations (0.0; 0.2; 0.4; 0.6 and 0.8 mL L-1) and 15 replications. Among the indicators evaluated were the plant length, leaf number, stem diameter, root length, and fresh and dry biomass. The results showed that the 0.4 mL L-1 VIUSID Agro® concentration produced greater growth than the control (0.0 mL L-1) for variables analyzed plant length, fresh and dry biomass of aerial portion, root length, fresh and dry biomass of root. The foliar application of VIUSID Agro® improved the performance of the lettuce morphological characteristics in a hydroponic cultivation system. Although this is the first Brazil research evaluating VIUSID Agro® as an organic fertilizer in hydroponic lettuce, results have shown promise.
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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".