Growth Promoting Microorganisms for Treatment of Soybean Seeds
Bibliographic record
Abstract
The use of microorganisms that promote plant growth is an important tool for increasing agricultural production; besides, there is an emerging demand for decreasing utilization of chemical fertilizers and to solidify sustainable agriculture. The objective of this study was to evaluate the efficacy of growth promoting microorganisms Bradyrhizobium elkanii and Trichoderma harzianum for treatment of soybean seeds. This experiment was conducted at the Regional Institute of Rural Development (IRDeR), in Augusto Pestana, RS, Brazil. The experimental design was randomized blocks and treatments were: control, Trichoderma harzianum, Bradyrhizobium elkanii, and co-inoculated Trichoderma harzianum + Bradyrhizobium elkanii, with four replications per treatment. The sample units were five rows 5 m in length, spaced at 0.5 m, and three central lines were used to analyze the components of soybean yield. Of these, two lines were harvested 4 m, and the incidence of soil diseases in the plants was evaluated in one of the lines. Treatments means were compared using Tukey test. There was an effect of treatments: B. elkanii and treatment with co-inoculation B. elkanii and T. harzianum showed a significant response to grain yield. The chemical treatment without microorganisms with a biological effect had a higher incidence of diseases and a lower yield of grains.
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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 | 0.000 |
| 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".