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Record W2570923512 · doi:10.5937/aaser1642155v

Organic viticulture in world, Serbia and region

2016· article· en· W2570923512 on OpenAlexaboutno aff
V. Vukosavljević, S. Todić, Saša Matijašević

Bibliographic record

VenueActa agriculturae Serbica · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsnot available
Fundersnot available
KeywordsViticultureOrganic farmingAgricultureMediterranean climateOrganic productionAgroforestryGeographyWineVineyardEnvironmental protectionBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.223
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2016
Admission routes1
Has abstractyes

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