Study of morphology, chemical, and amino acid composition of red deer meat
Bibliographic record
Abstract
AIM: The aim of this study was to evaluate red deer (maral) meat quality based on chemical composition, pH, water-binding capacity (WBC), and amino acid content. MATERIALS AND METHODS: Maral meat surface morphology measurements were obtained by scanning electron microscopy. Active acidity (pH) was determined by potentiometry. Samples were analyzed for WBC by exudation of moisture to a filter paper by the application of pressure. Chemical composition (moisture, protein, fat, and ash fractions) was obtained by drying at 150°C and by extraction, using ethylic ether, and ashing at 500-600°C. The amino acid composition was obtained by liquid chromatography. RESULTS: Maral meat, with a pH of 5.85 and an average moisture content of 76.82%, was found to be low in fat (2.26%). Its protein content was 18.71% while its ash content was 2.21%. The amino acid composition showed that lysine (9.85 g/100 g), threonine (5.38 g/100 g), and valine (5.84 g/100 g) predominated in maral meat, while phenylalanine (4.08 g/100 g), methionine (3.29 g/100 g), and tryptophan (0.94 g/100 g) were relatively low in maral meat compared to other meats. The average WBC was found to be 65.82% and WBC was found to inversely correlate with moisture content. CONCLUSION: Low-fat content, high mineral content, and balanced amino-acid composition qualify maral meat as a worthy dietary and functional food.
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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 | 0.001 | 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) | 0.001 | 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".