Organic chromium supplementation in the diet of three porcine genotypes with different growth potential: effects on growth, metabolites, hormones, and carcass traits
Bibliographic record
Abstract
Chromium (Cr) potentiates insulin, influences the weight gain composition and carcass through changes in metabolites and hormones. The aim was to analyze the performance, carcasses, metabolites, and hormones of three porcine genotypes, G1 = Mexican Hairless × Yorkshire, G2 = Asian × Mexican Hairless, and G3 = Asian × Yorkshire, males and females with dietary Cr-L methionine (Cr-Met) (0.250 mg kg −1 DM). The experimental design was completely randomized factorial (3-genotypes × 2-sex × 2-Cr levels). The fattening with 84 pigs, live weight (LW) = 20.65 ± 0.68 kg, lasted 126 days, and were slaughtered. Thirty-six pigs were sampled to analyze metabolites and hormones in blood. The G1 ingested more ( P = 0.04) total and daily dry matter (DMTI, DDMI). G2 and G3, and Cr-Met improved daily weight gain (DWG) and feed conversion (FC) ( P = 0.05). Cr-Met reduced glucose ( P = 0.05). Genotype, sex, and Cr-Met did not affect hormone levels ( P > 0.05). G3 and Cr-Met increased HCW and CCW; Cr-Met reduced back fat ( P = 0.05). We concluded that G2 and G3 had better DWG and FC; Cr-Met improved DWG and FC. Cholesterol was different between genotypes and sexes; Cr-Met reduced the glucose level. The levels of hormones were similar. The HCW and CCW of G2 and G3, sows and Cr-Met were higher; Cr-Met reduced the BF12thR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".