Effect of Saline Stress and Calcium Nitrate on Lettuce Grown on Coconut Fiber
Bibliographic record
Abstract
This study aimed to evaluate the use of saline solutions enriched with calcium nitrate in the production of lettuce grown in coconut fiber. The experiment was carried out from July to August 2017 in a greenhouse, at the Federal Rural University of the Semi-Arid Region (UFERSA), Mossoró-RN, Brazil. A randomized block design was used, in 2 × 5 factorial scheme, with three replicates. Treatments resulted from the combination of two lettuce cultivars [Elba (Curly) and Irene (Crisphead)] and five nutrient solutions (S1- standard nutrient solution; S2-S1 + NaCl (28.48 mmol L-1); S3-S2 + Ca(NO3)2 (6.89 mmol L-1); S4-S2 + Ca(NO3)2 (9.15 mmol L-1); S5-S2 + Ca(NO3)2 (11.43 mmol L-1)]. Plants were harvest 30 days after transplantation and the following variables were analyzed: head diameter, stem diameter, number of leaves, fresh weight, dry weight, leaf area, specific leaf area and leaf succulence. The cv. Irene (Crisphead) is more tolerant to nutrient solution salinity compared with the cv. Elba (Curly). Nutrient solutions enriched with 50 and 100% of Ca(NO3)2 promoted better performance of the cultivars Elba and Irene, respectively, fertigated with saline nutrient solution.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".