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Record W2319960062 · doi:10.1021/jf204577g

Effects of Genotype, Latitude, and Weather Conditions on the Composition of Sugars, Sugar Alcohols, Fruit Acids, and Ascorbic Acid in Sea Buckthorn (Hippophaë rhamnoides ssp. mongolica) Berry Juice

2012· article· en· W2319960062 on OpenAlexaff
Jie Zheng, Baoru Yang, Martin Trépanier, Heikki Kallio

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2012
Typearticle
Languageen
FieldMedicine
TopicPhytochemical and Pharmacological Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMalic acidFructoseAscorbic acidSugarQuinic acidHippophae rhamnoidesSucroseBerryChemistryFood scienceComposition (language)BotanyCitric acidHorticultureBiologyBiochemistry

Abstract

fetched live from OpenAlex

Sea buckthorn berries (Hippophaë rhamnoides ssp. mongolica) of nine varieties were collected from three growth locations in five inconsecutive years (n = 152) to study the compositional differences of sugars, sugar alcohols, fruit acids, and ascorbic acid in berries of different genotypes. Fructose and glucose (major sugars) were highest in Chuiskaya and Vitaminaya among the varieties studied, respectively. Malic acid and quinic acid (major acids) were highest in Pertsik and Vitaminaya, respectively. Ascorbic acid was highest in Oranzhevaya and lowest in Vitaminaya. Berry samples of nine varieties collected from two growth locations in five years (n = 124) were combined to study the effects of latitude and weather conditions on the composition of H. rhamnoides ssp. mongolica. Sea buckthorn berries grown at lower latitude had higher levels of total sugar and sugar/acid ratio and a lower level of total acid and were supposed to have better sensory properties than those grown at higher latitude. Glucose, quinic acid, and ascorbic acid were hardly influenced by weather conditions. The other components showed various correlations with temperature, radiation, precipitation, and humidity variables. In addition, fructose, sucrose, and myo-inositol correlated positively with each other and showed negative correlation with malic acid on the basis of all the samples studied (n = 152).

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.251
Teacher spread0.236 · 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

Citations54
Published2012
Admission routes1
Has abstractyes

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