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Record W3135041865

Development of grapes and wine-making industry of Moldova on the basis of modern achievements of science and innovations

2020· article· en· W3135041865 on OpenAlexaboutno aff
Boris Gaina, Svetlana Fedorchukova, Galina Gobirman

Bibliographic record

VenueMunich Personal RePEc Archive (Munich University) · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsnot available
Fundersnot available
KeywordsWinemakingWineVineyardBiotechnologyEconomyPolitical scienceAgricultural economicsEnvironmental protectionBusinessEngineeringGeographyHorticultureEconomicsBiologyFood science
DOInot available

Abstract

fetched live from OpenAlex

The wine-growing and wine-making complex of Moldova has come a long way: from metal-intensive technical equipment from unalloyed steels to modern European equipment from stainless, food-grade material. All new vineyard plantings are created from planting of certified material of our own production or acquired in Italy, France
\nand Germany. At the present stage, vineyard plantations in Moldova are 80% occupied by classic European varieties. The rest - are local indigenous varieties. All technological processes in primary winemaking are based on modern biotechnology achievements and innovations: enzymes for clarifying wort, yeast for fermentation and bacteria to reduce acidity in red wines. The well-known preservative - sulfur dioxide is replaced with inert gases (nitrogen, carbon dioxide) and the use of low temperatures. Table wines are exported mainly to Romania, the Czech Republic, Germany, Poland, China, the United States and Canada. All the achievements of viticulture and winemaking in Moldova are based on the latest scientific and technological progress, developed and implemented in the Republic of Moldova by scientists from the Academy of Sciences of Moldova, universities, as well as specialists from the National Office 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 score0.714
Threshold uncertainty score0.393

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.064
GPT teacher head0.250
Teacher spread0.185 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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Same venueMunich Personal RePEc Archive (Munich University)Same topicHorticultural and Viticultural ResearchFrench-language works237,207