Study of morphology, chemical, and amino acid composition of red deer meat
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 it