Effects of feeding modified tall oil and supplemental potassium and magnesium on growth performance, carcass characteristics, and meat quality of growing-finishing pigs
Bibliographic record
Abstract
Eighty crossbred gilts (initially 45.9 kg) were allotted randomly to one of four dietary treatments by weight and ancestry. The trial was arranged as a 2 × 2 factorial with two levels of modified tall oil (MTO) (0 or 0.50%) and added K2SO4-2MgSO4 (0 or 2%), equating to daily K and Mg intakes of 10.84 and 7.75 g, respectively. The corn-soybean meal diets were fed in two phases [45.9 to 76.2 and 76.2 to 118.1 kg body weight (BW)], and supplemental K/Mg was added in place of corn for the final 7 d preslaughter (starting at 114.1 kg BW). Dietary treatment did not affect (P > 0.10) average daily gain (ADG), average daily feed intake (ADFI), or gain to feed ratio (G/F). Feeding MTO decreased average backfat (P = 0.05) and increased intramuscular marbling (P = 0.04). Modified tall oil increased (P = 0.02) percentage lean, and K/Mg supplementation lowered (P = 0.04) longissimus muscle glycogen content. Dietary treatment did not affect (P > 0.10) other carcass characteristics or measures of meat quality. Feeding MTO increased plasma glucose (P = 0.05) and decreased (P = 0.10) base excess in the extracellular fluid. Feeding K/Mg decreased (P < 0.10) plasma pH, BUN, and base excess in the whole-blood and extracellular fluid and increased (P < 0.10) ionized Mg++ and lactate. These results support earlier research identifying MTO as a carcass modifier and contributor to meat composition and quality. Potassium and Mg supplementation altered whole-blood profiles and longissimus muscle glycogen content in a manner expected to improve pork quality, although not observed. Key words: Swine, modified tall oil, potassium, magnesium, meat quality
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".