Turkish Tombul Hazelnut (<i>Corylus avellana</i> L.). 1. Compositional Characteristics
Bibliographic record
Abstract
The quality of Tombul (Round) hazelnut, grown in the Giresun province of Turkey, was determined by measuring proximate composition, minerals, vitamins, dietary fiber, amino acids, and taste active components (free amino acids, sugars, and organic acids). Fat was the predominant component in Tombul hazelnut (approximately 61%). The major minerals were potassium, phosphorus, calcium, magnesium, and selenium. Hazelnut was also found to serve as an excellent source of vitamin E (24 mg/100 g) and a good source of water soluble (B complex) vitamins and dietary fiber. The major amino acids were glutamic acid, arginine, and aspartic acid. The three nonessential amino acids and the essential amino acids contributed 44.9 and 30.9% to the total amino acids present, respectively, while lysine and tryptophan were the limiting amino acids in Tombul hazelnut. Twenty-one free amino acids, six sugars, and six organic acids were positively identified; among these, arginine, sucrose, and malic acid predominated, respectively. These taste active components may play a significant role in the taste and flavor characteristics of hazelnut. Thus, the present results suggest that Tombul hazelnut serves as a good source of vital nutrients and taste active components.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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.
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".