Magnesium Sources and Inoculation with Azospirillum brasilense in the Initial Development of Maize
Bibliographic record
Abstract
The aim was to evaluate the initial development of maize plants when submitted to mineral fertilization with magnesium sources in the presence or absence of seed inoculation with Azospirillum brasilense. To do so, was conducted an experiment in greenhouse. In the essay was adopted a randomized blocks design, in a factorial scheme 3 × 2, represented by the magnesium sources: magnesium sulfate (MgSO4), magnesium oxide (MgO) and control without Mg fertilization, in the presence or absence of inoculation with A. brasilense. The Mg sources were supplied with a dose of 30 kg ha-1, being homogenized in the substrate before sowing the crop. The analysis were carried out in the V4 stage, evaluating: basal stem diameter (BSD); relative chlorophyll content (SPAD index), leaf dry mass (LDM), stem + sheath blade dry mass (SSDM) and root dry mass (RDM). No differences were observed for the factors interaction and for the Mg sources. When considered the seed inoculation there was increases of 7.1%; 6.61%; 19.23%; 28.32%; and 15.17 %, for basal stem diameter, SPAD index, leaf dry mass, stem + sheath blade dry mass and roots, respectively. The inoculation of maize seeds with A. brasilense increases the initial development of maize plants and the SPAD index in greenhouse conditions in the V4, while the fertilization with the magnesium sources do not interfere in the maize development.
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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) | 0.001 | 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".