Vertical Distribution of Pea (Pisum sativum L.) Seed Yield Depending on the Applied Bacterial Inoculants
Bibliographic record
Abstract
Among legumes, pea (Pisum sativum L.) is the second most important grain legume crop in the world, which is widely used both in human nutrition and as fodder. The yield potential of cultivars is one of the major factors that determine the use of field pea. Currently, pre-sowing inoculation of pea seeds is a promising treatment and is one of agronomic solutions for sustainable agriculture development. The objective of the research was to estimate the productivity of the ‘afila’ and ‘semileafs’ morphotypes of field pea, depending on different inoculants based on symbiotic bacteria (Rhizobium)-commercial (NitragineTM) and noncommercial, produced by the Polish Institute of Soil Science and Plant Cultivation (IUNG). The research was based on the precise field experiment, conducted in four replicates and carried out in the experimental field of Bayer® company located in Modzurów, Silesian viovodeship. The experimental field soil was classified as Umbrisol-slightly degraded chernozem, formed from loess. The examined inoculants were applied during sowing. The presented results of the studies on the symbiotic nitrogen fixation by leguminous plants indicate that the productivity of pea was positively affected by the application of IUNG (noncommercial) inoculant. On the other hand, it is not recommended to use NitragineTM separately, as it inhibits the growth of pea. Plants of the ‘Klif’ variety used the symbiotically fixed nitrogen more effectively and demonstrated higher yield component and better phenotypic parameters.
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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".