Use of Senna uniflora as Organic Fertilizer in the Production of Lettuce in the Brazilian Semiarid
Bibliographic record
Abstract
The use of plant resources available on the farm, and of great relevance to the family farmers of the Northeastern semi-arid region, Brazil. The experiment was carried in the experimental area of the agricultural science center, Universidade Federal Rural do Semi-árido (UFERSA), Mossoró, Brazil, with the objective of evaluating use of Senna uniflora as organic fertilizer in the production of lettuce in the Brazilian semiarid, from October 2014 to February 2015. The experimental design of randomized complete blocks with the treatments arranged in 4 × 4 factorial scheme, with three replicates. The first factor consisted of amounts S. uniflora (0, 1.8, 3.6, and 5.4 kg m-2 of dry matter) with four incorporation times into the soil (0; 28; 56 and 84 days before transplanting lettuce). The transplanted lettuce cultivar went was the “Elba”. The evaluated characteristics were the following: plant height, diameter plant, number of leaves per plant, green mass production and dry mass production. The best agronomic efficiency was obtained with soil incorporation of 5.4 kg m-2 in the incorporation period of 56 days after transplanting, with phytomass production of 235.2 g plant-1. S. uniflora becomes a viable option to be used as an organic fertilizer in lettuce production.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".