Effects of dietary sunflower seeds on lactation performance and conjugated linoleic acid content of milk
Bibliographic record
Abstract
The conjugated linoleic acid (CLA) concentration in milk and the lactation performance of cows fed either a control (CON) or a sunflower seed [SS; 7% of dietary dry matter (DM)] containing diet were compared in a 12-wk lactation trial, starting from week 3 after calving, with 17 multiparous and 8 primiparous cows. The CON and SS diets were isonitrogenous and provided 4 and 6% crude fat in the total mixed rations and were fed to 13 and 12 cows, respectively. Daily DM intake (DMI ), milk production and weekly body weights were recorded. Milk samples collected weekly at four consecutive milkings were individually analyzed for fat, protein and lactose content, and fatty acid composition. The DMI and milk production of the cows fed t he CON and SS diets were 20.5 ± 0.80 and 20.2 ± 0.80 kg d-1 (P > 0.05) and 38.2 ± 1.71 and 38.2 ± 1.71 kg d-1 (P > 0.05), respectively. No differences due to diet were observed for cow body weight, body condition score, or for content and yield of milk fat, protein and lactose. The CLA cis-9, trans-11 concentration in milk from cows fed the CON and SS diets was 3.9 and 7.9 mg g-1 fatty acids (P < 0.01), respectively. The average CLA cis-9, trans-11 yield in milk from cows fed CON and SS diets was 5.1 ± 0.07 and 10.9 ± 0.07 g d-1, respectively, or 114% greater (P < 0.05) for cows fed the SS diet. The study indicates that sunflower seed inclusion at 7% of dietary DM to dairy cows increases the CLA concentration and yield in milk, without affecting DMI, milk production or composition. Key words: Conjugated linoleic acids, cow, milk, sunflower seed
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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 | 0.001 |
| 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".