Agronomic Efficiency of Signum Inoculant in Pre-inoculation of Soybean at 35 and 20 Days Before Sowing in Treated Seeds
Bibliographic record
Abstract
Pre-inoculating soybean seeds can make sowing faster and provide additional benefits to farmers. However, it needs to guarantee the nitrogen supply to maintain the viability and sustainability of the technique. In this study, we evaluated the agronomic efficiency of SIGNUM® inoculant in the pre-inoculation at 20 and 35 days before sowing chemically treated soybean seeds. Experiments were conducted in four field experiments located at Paraná, Brazil, with a history of soybean cultivation managed under no-tillage systems, with crop rotation according to regional edaphoclimatic conditions. Agronomic efficiency in fields were compared with standard inoculation with a registered product used by farmers. Chemical treatment of soybean seeds with Standak Top® or Maxin XL® + Cruiser® associated with pre-inoculation of the inoculant SIGNUM® for 25 and 30 days reduced the concentration of viable Bradyrhizobium cells recovered from seeds. However, no significant difference was observed regarding nodulation, biological nitrogen fixation, and yield between the soybean inoculated with standard inoculation on farm or pre-inoculation with SIGNUM® in most studied areas.
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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 | 0.001 |
| 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".