Path Analysis in Soybean Cultivars Grown under Foliar Spraying and Furrow Inoculation with Azospirillum brasilense
Bibliographic record
Abstract
The objective of this study was to evaluate, through path analysis, the influence of agronomic characters as a function of foliar spraying and furrow inoculation by Azospirillum brasilense on soybean yield. Two experiments were conducted in the crop years of 2013/14 and 2014/15, grown in Lavras, Minas Gerais. In the first experiment, the experimental design was a randomized block in a factorial 4 × 6, four cultivars (Anta 82 RR®, BRS Favorita RR®, BRS 780 RR®, BRS 820 RR®) and six doses of A. brasilense (0, 300, 400, 500, 600, 700 mL ha-1), with three replications. In the second experiment, the experimental design was a randomized block design, arranged in a 4 × 2 factorial scheme, four cultivars (Anta 82 RR®, BRS Favorita RR®, BRS 780 RR®, BRS 820 RR®) and two treatments with A. brasilense (inoculated and non-inoculated) with three replications. For both experiments, it was established plant height, phytomass of the aerial part, plant height at harvest, first legume insertion, number of legumes, number of grains per legume, mass of one thousand grains and grain yield. In the study with foliar spraying of soybean with A. brasilense, plant height at harvest was the only variable that had a direct effect on soybean grain yield. As such, in the study with furrow inoculation of A. brasilense in soybean, plant height at harvest and number of vegetables were the variables with the greatest direct effects on soybean grain yield.
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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".