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Record W2915219313 · doi:10.1051/bioconf/20191203003

Progress for research of grape and wine culture in Georgia, the South Caucasus

2019· article· en· W2915219313 on OpenAlexaff
D. Maghradze, Andro Aslanishvili, I. Mdinaradze, D. Tkemaladze, Levan Mekhuzla, David Lordkipanidze, Mindia Jalabadze, Eliso Kvavadze, Nana Rusishvili, Patrick E. McGovern, Patrice This, Roberto Baciliéri, O. Failla, Gabriele Cola, Luigi Mariani, Silvia Laura Toffolatti, Gabriella De Lorenzis, P.A. Bianco, F. Quaglino, Nathan Wales, M. Thomas P. Gilbert, Laurent Bouby, Tina Kazeli, L. Ujmajuridze, L. Mamasakhlisashili, Stephen Batiuk, Ashley Graham, Elisabetta Boaretto, Ana Cheishvili, and L. Davitashvili

Bibliographic record

VenueBIO Web of Conferences · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGeorgianWineOenologyMultidisciplinary approachAgency (philosophy)ViticultureGeographyArchaeologyPolitical scienceSocial scienceSociologyBiology

Abstract

fetched live from OpenAlex

This communication will provide the latest information about the progress of the “Research Project for the Study of Georgian Grapes and Wine Culture”, managed by the National Wine Agency of Georgia since 2014. Local and foreign institutions continue to work together with the aim of stimulating multidisciplinary scientific research activity on Georgian viticulture and viniculture and to reconstruct their development from Neolithic civilizations to the present. The project is multidisciplinary in nature, merging contributions from archaeology, history, ethnography, molecular genetics, biomolecular archaeology, palaeobotany, ampelography, enology, climatology and other scientific fields.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.227

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.113
GPT teacher head0.356
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations14
Published2019
Admission routes1
Has abstractyes

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