The analysis of selected physical and technological parameters of pork quality depending on intesity of the pigs growth in fattening
Bibliographic record
Abstract
Article Details: Received: 2020-10-30 | Accepted: 2020-11-27 | Available online: 2021-01-31 https://doi.org/10.15414/afz.2021.24.mi-prap.71-74 The experiment was conducted to compare the differences in the physical and technological quality of pork meat with different growth intensity. The pigs were divided in accordance with the average daily gain values as followed: a) the fast-growing group (R1> AGD + 0.5 SD), b) the medium-fast-growing group (R2= AGD ± 0.5 SD) and c) slow-growing group (R3< AGD - 0.5 SD). For group of gilts, we found a statistically significant difference (P ≤ 0.05) in the drip loss value between the fast-growing group and the medium-growing group and the fast-growing group compared to the slow-growing group of gilts. Between the fast-growing group and the medium-growing group, as well as between the fast-growing group and the slow-growing group of gilts, the differences in shear force value were statistically highly significant at the level of P ≤ 0.01. At the same time, in the colour of meat in redness (a* value) were found statistically significant differences between groups of barrows according to the growth rate at the level of P ≤ 0.05 and between fast and slow-growing gilts at the level of P ≤ 0.01 and medium and slow-growing gilts at the level of P ≤ 0.05. In addition, in the meat yellowness (b*) we also determined a statistically highly significant difference at the level of P ≤ 0.01 between the fastest-growing group and medium fast-growing barrows and a significant difference at the level of P ≤ 0.05 between the fast and slow-growing group of gilts. Keywords: fattening pigs, growth intensity in pig, pork quality References Brocks, L. et al. (1998). Histochemical characteristics in relation to meat quality properties in the Longissimus lumborum of fast and lean growing lines of Large White pigs. Meat Science , 50(4), 411–420. DOI: 10.1016/s0309-1740(98)00053-9 Correa, J.A. et al. (2006). 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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".