Path Analysis in Soybean Under Drought Stress and Co-inoculated With Azospirillum brasilense
Bibliographic record
Abstract
The study was carried out with the objective of verifying the effect of the bacterium Azospirillum brasilense in the behavior of the morphological characteristics of cultivars soybean submitted to drought stress. Two experiments were carried in randomized blocks with a split-split plot design, with four replications. We evaluated the cultivars TMG 132 and ANTA 82. The treatments were subjected to two irrigation depths: total irrigation (100% of crop evapotranspiration) and irrigation with drought stress (25% of crop evapotranspiration) and co-inoculated with Azospirillum brasilense and inoculated whit only Bradyrhizobium japonicum. The coefficients of the correlation between the characteristics: leaf area, plant height, root length, number of nodules, shoot dry matter, root dry matter and nodule dry matter were analyzed, as well as the effects over the productivity through the path analisys. The drought stress did not alter the correlations between the studied characteristics; however the alteration happened due to the inoculation method. The path analysis showed that the root length was the only variable that had a direct effect on soybean grain yield, and this occurred only inoculation. In the co-inoculated treatments with Azospirillum brasilense, there were greater indirect effects in grain yield due the root dry matter and shoot dry matter.
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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".