The effect of carcass weight on chemical characteristics and fatty acid composition of <i>Longissimus dorsi</i> and <i>Semimembranosus</i> muscles of European wild boar (<i>Sus scrofa scrofa</i>) meat
Bibliographic record
Abstract
The objective of this study was to investigate the effect of carcass weight on chemical characteristics and fatty acid composition of m. longissimus dorsi and m. semimembranosus of European wild boar (Sus scrofa scrofa) meat. The research was carried out on 50 wild boars shot during four battues at hunting reserves in the central part of Poland. The carcasses were divided into three groups: ≤30 kg (G1), 31–45 kg (G2), and >45 kg (G3). The protein concentration increased with carcass weight (P ≤ 0.01). The concentration of saturated fatty acids was influenced by carcass weight and was higher when weight increased. A significantly higher level of monounsaturated fatty acids (MUFA) was found in the carcasses of wild boars from G3 compared with G1 group (P ≤ 0.01). The content of C18:2n-6 was significantly lower in the lightest carcasses compared with the heaviest (P ≤ 0.05). Statistical analysis confirmed that carcasses weight significantly affected polyunsaturated fatty acids (PUFA) n-3 content. The lightest carcasses contained a significantly higher concentration of PUFA n-3 (P ≤ 0.01). In addition, in G3, the amount of C22:5n-3 was two-fold lower (P ≤ 0.01) than in G1. The research has shown that the meat quality was affected by the carcass weight, resulting better parameters in the lighter than in the heavier carcasses. Therefore, most of the culled wild boars should be squeakers and young boars after the first year of life.
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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) | 0.001 | 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".