Application of microbiological fertilizers in viticulture: Grape yield and quality of wine cv. Riesling
Bibliographic record
Abstract
The recommended cultivars for top quality wines Riesling in the vineyards of Grocka is in full crop. It was grafted on Kober 5 BB stock and planted on the soil type cambysoil. The content of total nitrogen is 0.1-0.15%. Supply of easily available potassium varies between 12.3-15 mg/100g a.d.s.2, i.e. phosphorus 0.4-3.6 mg/100g a.d.s. in layer up to 40 cm. Microbiological fertilizer was used in the study - biological preparation prepared with mixed natural populations Azotobacter chroococcum, Bacillus megaterium and Bacillus circulons. The space in row is idle land and the space between rows was sown each year (March-April) with a mixture of field pea and barley and ploughed in the inflorescence phase of legumes. Grape yield varied between 8772-6804 kg/ha. Microbiological fertilizer with Azotobacter had the highest yield and the control treatment had the lowest yield, where only grass mixture was sown. Extremely dry climatic conditions in the trial period caused the grape yield in cv. Riesling to be extremely low. In combination of fertilizers Bacillus megaterium + Bacillus circulons wine with the most ethanol, extracts and polyphenols was obtained. The wine obtained from the control treatment had a typical taste.
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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".