PSXIV-7 Red blood cells are a better indicator than plasma for predicting subcutaneous trans fatty acid composition in cattle
Bibliographic record
Abstract
Abstract The trans (t)-18:1 in beef has become of interest as partially-hydrogenated-vegetable-oils are removed from the food supply. Predicting t-18:1 in beef early in the feeding period would be useful if limitations are put on t18:1 in beef. The objective of the present study was to determine which blood component is a better predictor of t18:1 in beef. Accordingly, proportions of t10-18:1 and t11-18:1 (i.e. major t-18:1 isomers) were measured in heifer red blood cells (RBC) and plasma (2 pens of 7 animals) after 0, 28, 56 and 76d on a barley-grain-based diet, and also correlated with post-slaughter subcutaneous fat. Blood components were subjected to direct base then acid methylation, and subcutaneous fat to base methylation after freeze drying. c10-17:1 methyl ester was included as an internal standard. Fatty acid methyl esters were purified using Superclean LC-SI-SPETM and then analyzed by GC using a 100m CP-Sil88 column. Fatty acids were analyzed using the Mixed Models procedure of SAS (v 9.3; SAS Institute, Cary, IN) with time as a repeated measure, and pen as a random factor. Pearson correlation coefficients were generated using the PROC CORR procedure of SAS. Proportions of total t-18:1 declined in both RBC and plasma during later stages of finishing (P < 0.05). At 28d, t11-18:1 decreased and t10-18:1 increased in RBC and plasma (P < 0.05). By 76 d, proportions of t10-18:1 declined in both RBC and plasma to 0 d levels. RBC and plasma t-18:1 compositions were highly correlated for all time points and overall (R≥0.7, P ≤ 0.02). Correlations with post slaughter backfat were, however, consistently greater for RBC compared to plasma. The use of RBC t-18:1 composition may, therefore, be superior for predicting t-18:1 composition in other tissues, and the length of finishing on barley could be useful for manipulating the composition and content of t18:1 in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".