PSVII-7 Fatty acid profile is not affected by feeding finishing cattle essential oils and/or benzoic acid
Bibliographic record
Abstract
Abstract Fatty acid profile of beef subcutaneous fat was evaluated in two separate studies where blends of essential oils and/or benzoic acid were fed to finishing steers. Study 1 had sixty-eight finishing steers that were fed 1 of 5 finishing diets: no additional supplement, monensin/tylosin, essential oil blend #1 (Victus Liv, DSM Nutritional Products), benzoic acid (VevoVitall, DSM Nutritional Products), or a combination of essential oil blend #1 and benzoic acid. Study 2 had seventy-six finishing steers that were fed 1 of 7 finishing diets: no additional supplement, monensin/tylosin, essential oil blend #1, essential oil blend #2 (Fortissa Fit 45, Provimi Canada), benzoic acid, a combination of essential oil blend #1 and benzoic acid, or a combination of essential oil blend #2 and benzoic acid. All feed additives were supplemented according to manufacturer instructions, which were 33 mg/kg of monensin, 11 mg/kg of tylosin, 1 g/steer/day of essential oil blend #1, 4 g/steer/day of essential oil blend #2, and 0.5% inclusion level (on a DM basis) of benzoic acid. Fatty acid profile was determined on a 4 cm × 1 cm × 1 cm sample of subcutaneous fat from the 12th rib location of each steer. Fatty acid methyl esters were used to identify and quantify individual fatty acids using gas chromatography. Data were analyzed separately for the two studies, using a RCBD with fixed effect of treatment, and random effects of block (allocation weight of the steers) and the interaction of treatment and block. Total saturated fatty acids (SFA), total monounsaturated fatty acids (MUFA), total polyunsaturated fatty acids (PUFA), MUFA:SFA, PUFA:SFA, and n6:n3 were not different (P > 0.09) between treatments for either study. Overall, it was concluded that supplementation of essential oils and/or benzoic acid did not affect fatty acid profile of beef subcutaneous fat.
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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".