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Record W3000612761

[STUDY OF LIPIDS OF THE FRUITS OF USUAL HAZEL-NUT CORYLUS AVELLANA L., GROWING IN GEORGIA].

2017· article· en· W3000612761 on OpenAlexaff
Bela Kikalishvili, Nana Gorgaslidze, Zurabashvili Dz, Ts Sulakvelidze, M Malania, Durmishkhan Turabelidze

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldNursing
TopicNuts composition and effects
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsNutChemistryFood scienceUnsaturated fatty acidFatty acidBotanyHorticultureBiologyOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

The aim of this investigation was the study of lipids from the fruits of usual hazel-nut Corylus avellana L, growing in Georgia. Ripe fruits was collected in the West Georgia, just in Imereti. From the powdered fruits was obtained the sums of neutral and pollar lipids. Qualitatively there were established classes entered in them. By using High performance liquid chromatography qualitatively and quantitatively were identified ten fatty acids, which time of deduction hesitate from 4,01 min to 13,00 min. By the analyses there were determined unsaturated fatty acids C12:0 to C24:0. The content of unsaturated fatty acids considerably is distinquished from the content of the oil from the hazel-nut, growing in the other eco-geographical conditions. In the oil of the hazel-nut growing in Georgia content of hexadecanoic acid is by far exceled (surpassed) than of the oil from the nut growing in the other natural conditions. In the other matters dominant acid is octadecanoic acid. The oil from the fruits of hazel-nut content physiologically active compounds, which desirably correlation is interesting not only for receiving (obtaining) cosmetic means, not is important for usage in practical medicine.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.260
Teacher spread0.231 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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