Biostimulants Based on Humic Acids, Amino Acids and Vitamins Increase Growth and Quality of Lettuce Seedlings
Bibliographic record
Abstract
Humic acids (HA), amino acids (AA) and vitamins can increase plant growth; however, there are still few studies that determine the ideal concentration for use in lettuce (Lactuca sativa L.) production. Research was conducted to evaluate the effects of HA enriched with sources and levels of AA and vitamins on lettuce seedlings morphology and quality. Two experiments were conducted in a completely randomized design with four replicates. The first experiment had 16 treatments, 3 × 5 + 1 factorial design, the first factor was betaine, glycine and proline used in the HA enrichment, and second factor five concentrations of AA (0, 0.5, 1.0, 1.5, and 2.0 g L-1), plus the control, without HA and AA. The second experiment had 21 treatments, 4 × 5 + 1 factorial design, the first factor was the use of four vitamins (A, E, B1 and B2) in the enrichment of HA, while the second factor was composed of five concentrations of each vitamin (0, 0.5; 1.0, 1.5, and 2.0 g L-1) plus the control, without HA and vitamins. The application of HA and HA+AA promote the improvement in the seedling’s morphology and quality, while the application of the HA+vitamins negatively affected the morphology, reducing seedling quality.
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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) | 0.000 | 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".