Organic viticulture in world, Serbia and region
Bibliographic record
Abstract
organic production is a process that develops a viable and legitimate agricultural ecosystems. The link between man, agriculture and local environmental conditions, provides quality food for people, plants and animals in a healthy environment. There are a few basic principles and ideas that explain the production of organic food in the ecosystem. The organic viticulture production in which there is a balance between environmental conditions, varieties and methods of cultivation. The two main factors that determine the choice of location concepts and establishing assortments. Organic viticulture is widespread mainly in the Mediterranean countries of Europe such as Italy, France and Spain. The Mediterranean climate with hot, dry summers and constant air currents limiting the development of pests and diseases on grapevines. The same concept can be seen in California, USA and South Africa. In the wine-growing countries with humid climates apply to another concept of organic viticulture, and that is the use of varieties resistant to pests and diseases. In Romania, Germany, Hungary, Switzerland and northern France are represented interspecific hybrids and new resistant varieties. In the north-east of America, on the border with Canada, grown varieties Vitis labrusca and Vitis species in the south rotundifolia. However, in these humid regions is crucial in the selection of sites with appropriate climatic and soil conditions. The choice of suitable climatic conditions is important for conventional viticulture in order to get the best quality of grapes and wine.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".