Comparison on the fatty acid profiles of liver, subcutaneous fat and muscle from feedlot steers finished on diets supplemented with or without cinnamaldehyde or monensin
Bibliographic record
Abstract
BACKGROUND: Cinnamaldehyde (CIN) is the main active component of cinnamon (Cinnamomum cassia) oil and has been tested as alternative feed additive in cattle production. Little information was available on the effect of dietary CIN in comparison to monensin (MO) on beef fatty acid (FA) profile. This study analyzed FA profiles of liver, subcutaneous fat and pars costalis diaphragmatis (PCD) muscle obtained from steers (n = 70) finished on diets: control, a barley grain-silage feedlot diet; 330 mg/head.day MO; and 400, 800 or 1600 mg/head.day CIN treatments. RESULTS: Inclusion of MO or CIN did not affect total saturated, unsaturated, polyunsaturated FA and individual FA in the various tissues with exceptions that proportion of palmitic acid in PCD muscle was increased by 800 mg/steer.day CIN (P < 0.05). There were positive correlations (P < 0.05) on oleic, linoleic, conjugated linoleic acid (CLA)-c9,t11 and 18:1-t10 between the subcutaneous fat and PCD muscle, and on α-linolenic acid, CLA-c9,t11 and 18:1-t10 between PCD muscle and liver, whereas correlations on the FA between the subcutaneous fat and liver were not significant except for 18:1-t10 (P < 0.01). CONCLUSION: The results indicate that the supplementation of CIN and MO to feedlot diet has limited effect on beef FA profiles.
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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".