Effects of dietary additions of modified tall oil, chromium nicotinate, and L-carnitine on growth performance, carcass characteristics, and bacon characteristics of growing-finishing pigs
Bibliographic record
Abstract
Eighty gilts were supplemented with modified tall oil (MTO), chromium nicotinate (CrNic), and L-carnitine to determine effects on growth and meat quality characteristics. Pigs were assigned to one of eight treatments in a 2 × 2 × 2 factorial with main effects of MTO (0 or 0.5%), CrNic (0 or 50 μg kg-1), and L-carnitine (0 or 50 mg kg-1). Pigs fed MTO had increased (P = 0.03) average daily gain and pigs fed CrNic had improved (P = 0.02) gain:feed. Bellies from pigs supplemented with MTO with no CrNic were firmer (P < 0.05) than bellies from all other treatment combinations. No differences (P > 0.05) were detected for longissimus muscle (LM) visual or objective color values. Furthermore, no differences (P > 0.05) were detected for LM Warner-Bratzler shear force or sensory traits. Bacon from pigs fed MTO had firmer (P < 0.05) slices than bacon from pigs fed no MTO.These data suggest improvements in growth performance from addition of 0.50% MTO and(or) 50 μg kg-1 CrNic to diets of finishing gilts. Supplementing with MTO, CrNic, and L-carnitine had minimal effects on carcass, LM color and sensory, or bacon characteristics. Key words: Pork, modified tall oil, chromium nicotinate, L-carnitine, longissimus muscle, bacon
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".