Productivity of Lettuce Under Organic Fertilization
Bibliographic record
Abstract
Organic fertilizersare a viable alternative to reduce the expenses associated with synthetic fertilizers, besides improving the chemical, physical and biological attributes of the soil and promoting the increase of productivity in the cultivation of vegetables. The aim of this research was to evaluate the effect of goat manure applicatiosn on lettuce yield, cv. Cristina. The experiment was conducted at the Center for Agri-Food Science and Technology, Federal University of Campina Grande in the municipality of Pombal, PB, Brazil. The experiment was conducted in randomized blocks with treatments composed of five goat manure percentages (0, 25, 50, 75 and 100%), considering 100% of the recommended dose being 36.50 ton/ha de goat manure, in five replications, using a spacing of 0.25 × 0.25 m between plants. Harvesting was performed 30 days after transplanting the seedlings. The following parameters were analyzed: aerial part height, plant diameter, number of leaves, aerial fresh weight, root fresh weight, total fresh weight, aerial dry weight, root dry weight, total dry weight, root volume and productivity. The data were submitted to polynomial regression analysis. When the lettuce plants cv. Cristina were fertilized with 75% of the N ratio required for maximum production, the goat manure application produced the greatest development and increase productivity.
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) | 0.001 | 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 | 0.000 |
| 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".