Blood metabolites and fatty acid composition of milk and cheese from ewes fed oilseeds
Bibliographic record
Abstract
Sixteen lactating Dorset ewes were used in a completely randomized design to determine the effects of oilseed supplementation on milk yield and composition, blood metabolites, cheese yield and composition and fatty acid profile of milk and cheese. Four iso-nitrogenous diets were formulated: a control diet (CT) with no oilseed supplementation, a flaxseed supplemented diet (FS), a sunflower seed supplemented diet (SF), and a canola seed supplemented diet (CS). Oilseed-supplemented diets were formulated to contain 7% fatty acids. Feeding oilseeds had no effect on dry matter intake. Milk yield was similar for ewes fed CT, FS and SF and was higher (P < 0.05) than that of ewes fed CS. Ewes fed oilseed-supplemented diets produced milk with higher (P < 0.05) protein and total solid percentages than those fed CT. However, milk fat percentage was only higher (P < 0.05) in the milk of ewes fed FS and SF relative to those fed CT. Actual cheese yield was higher (P < 0.05) from milk of ewes fed oilseed-supplemented diets relative to those fed CT. However, cheese composition was not affected by dietary treatments. Plasma concentrations of fatty acids and cholesterol were increased (P < 0.05) while plasma concentration of β-hydroxybutyrate (βHBA) was decreased (P < 0.05) by oilseed supplementation. However, plasma concentrations of glucose and mammary extraction rate of plasma metabolites were not influenced by oilseed feeding. Oilseed supplementation increased (P < 0.05) concentrations of unsaturated fatty acids in milk fat. The order of increase was SF > FS > CS. Feeding SF, FS and CS increased (P < 0.05) conjugated linoleic acid (CLA) concentration in milk by 83.5, 39.2 and 16.5%, respectively. Concentration of linolenic acid in milk was only increased (P < 0.05) by feeding FS. Changes in milk fatty acid profiles were reflected in the cheese. Key words: Ewes, oilseeds, milk, cheese, fatty acids
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".