Muscle Fiber, Connective Tissue and Meat Quality Characteristics of Pork from Low Birth Weight Pigs as Affected by Diet-Induced Increased Fat Absorption and Preferential Muscle Marbling
Bibliographic record
Abstract
This study investigated how birth weight differences in piglets affected carcass and muscle fiber properties as well as meat quality at slaughter. Within litters, piglets were grouped according to their birth weight as either normal (NBW; 1.62-1.73 kg) or low (LBW; 1.18-1.29 kg). At 5 weeks of age, NBW piglets were randomly transitioned to control (C) or isocaloric high fat diets derived from non-dairy (HF), while LBW piglets were randomly transitioned to high fat diets derived from non-dairy (HF) or dairy sources (HFHD). Piglets were reared in individual pens under standardized housing and feeding conditions. Live weight was recorded weekly, and pigs were slaughtered at 12 weeks of age. Hot carcass weights, dressing percentages, lean meat yield, and primal cut proportions were determined. The m. longissimus thoracis was collected from the right side of the carcass for measurement of physical and chemical properties of meat and muscle fiber characteristics. Results indicated that LBW pigs compensated for their live weight compared to NBW pigs at 6 weeks of age. The mean muscle fiber diameter of LBW-HFHD group is significantly higher than NBW-C and NBW-HF group, and the type I muscle fiber diameter is significantly higher than NBW-C group. Dairy fat inclusion in LBW pig diet reduced carcass back fat thickness. This increased the calculated lean meat yield to be comparable to that of NBW pigs fed a commercial diet. Incorporating dairy-sourced high-fat into LBW pigs' diets appears to be an effective strategy for producing carcasses equivalent to NBW pigs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".