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
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".