Production of Hydroponic Lettuce under Different Salt Levels of Nutritive Solution
Bibliographic record
Abstract
The objective of this study was to evaluate the effects of different levels of salinity on the production of two cultivars of crisp lettuce under hydroponic cultivation. The research was performed in a protected environment at the Federal University of Campina Grande (UFCG), state of Paraíba, during in the period from 1 to 22 September 2016. The experimental design was completely randomized (CRD) which involved 4 × 2 factorial scheme with four levels of electrical conductivity of the nutrient solution (1.6, 3.6, 5.6 and 7.6 dS m-1) and two lettuce cultivars, Valentina (C1) and Alcione (C2), that totalized 8 treatments with 3 replicates. The evaluated variables were fresh and dry biomass of leaves, stems and roots. The results were subjected to analysis of variance by F-test (p < 0.05), the averages were compared by Tukey’s test (p < 0.01 and p < 0.05), and quantitative variables data were submitted to regression test. It was observed that the fresh leaves biomass and fresh stem biomass were significantly affected by salinity in all evaluated periods. However, cultivar factor singly presented significant statistical difference only for fresh root biomass. There was statistical interaction between the factors at 15 DAT for dry leaves biomass and at 21 DAT for dry stem biomass. It was concluded that, although some variables were significantly affected by the salinity of the nutritive solution, the hydroponic lettuce production was satisfactory in NFT system for electrical conductivity up to 3.5 dS m-1 for variables with commercial relevance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".