Multivariate Approach in the Initial Development of Soybean as a Function of Co-inoculation and Micronutrients
Bibliographic record
Abstract
Co-inoculation in soybean is the mixed inoculation with bacterias of the genus Bradyrhizobium and Azospirillum brasilense. The applicability of this practice has become the subject of recent research that aims to overcome the main limitations of biological nitrogen fixation, obtained by traditional soybean inoculation with only Bradyrhizobium. This study investigated the interaction effects among cultivars, bacterium types, with and without micronutrients applied to the seeds on the initial developmental stages of soybean cultivars using multivariate analyses. The seedlings were cultivated in pots filled with soil in greenhouse conditions. The experiment was installed in the Alta Mogiana branch of the Paulista Agribusiness Technology Agency (APTA) in Colina, SP. The 32 treatments were arranged as 4 × 4 × 2 factorial with four soybean cultivars, four bacterium types, with and without micronutrients applied to the seeds. The evaluations were performed at 5, 8 and 32 days after sowing (DAS). The parameters analyzed in the pots showed that the cultivars behaved differently depending on the type of bacteria used. The co-inoculation promoted better nodulation and initial seedling growth in some cultivars. In general, cultivars without application of micronutrients were superior in terms of the parameters analyzed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".