Agronomic Performance of Soybean With Seeds Treated With an Algae Extract Base Biostimulant
Bibliographic record
Abstract
The use of biostimulants in the treatment of soybean seeds can provide beneficial effects on the crop due to the synergism between the organic components present in products from natural compounds. Due to the scarcity of results of research on biostimulants in the treatment of soybean seeds, the objective of this study was to evaluate the effects of doses of a biostimulant in the treatment of seeds, on agronomic characteristics, productivity components and profitability of soybean culture. Therefore, two experiments were carried out in the field in the 2016/17 and 2017/18 harvests, using a randomized block design with six replications and four treatments referring to doses of 0.00; 0.05; 0.10; and 0.15 L 100 kg seeds-1. The agronomic characteristics of soybeans, grain yield and profitability of using the biostimulant were evaluated. From the obtained results, it can be seen that the dose of 0.15 L 100 kg seeds-1 provided greater profitability in both harvests. In addition, doses above 0.12 L 100 kg seed-1 provide higher grain yields and a higher grain mass, in addition to other productivity components.
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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) | 0.000 | 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".