Effect of feeding high-temperature, microtime-treated diets with different lipid sources on conjugated linoleic acid formation in finishing Hanwoo steers1
Bibliographic record
Abstract
The present study was conducted to examine the effects of different plant oils or plant oil mixtures and high-temperature, microtime processing (HTMT) on the CLA content in Hanwoo steers. Experiment 1, consisting of 3 in vitro trials, was conducted to determine how the biohydrogenation of C18 fatty acids and CLA production were affected by fat sources (tallow, soybean oil, linseed oil, or mixtures of soybean oil and linseed oil) or HTMT treatment in the rumen fluid. The results showed that HTMT was capable of protecting unsaturated fatty acids from biohydrogenation by ruminal bacteria. The HTMT-treated diet containing 4% linseed oil (LU) and a supplement containing 2% linseed oil and 1% soybean oil treated with HTMT + 1% soybean oil (L(2)S(1)U+S(1)) produced an increased quantity of trans-11 C18:1 and cis-9, trans-11 CLA, and a reduced quantity of trans-10, cis-12 CLA. Based on these results, in vivo studies (Exp. 2) were conducted with LU and L(2)S(1)U+S(1). These 2 treatments increased the content of cis-9, trans-11 CLA in LM compared with the control diet. The content of trans-10, cis-12 CLA in subcutaneous fat was also increased in the L(2)S(1)U+S(1) treatment compared with other treatments. The subcutaneous fat thickness in the LU treatment was decreased compared with the L(2)S(1)U+S(1) treatment. The LU treatment significantly decreased fatty acid synthase expression but simultaneously increased leptin expression. In this report, we showed that diets containing LU and L(2)S(1)U+S(1) were capable of increasing CLA in the intramuscular fat of beef.
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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 | 0.000 |
| 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".