Addition of protected and unprotected fish oil to diets for dairy cows. I. Effects on the yield, composition and taste of milk
Bibliographic record
Abstract
Thirty Holstein cows in mid-lactation (158+/-20 DIM) were given a total mixed ration based on grass silage, maize silage and rolled barley. After a preliminary period of 1 week, this diet was supplemented with nothing (control), unprotected fish oil (3.7% of dry matter, DM), or two levels of glutaraldehyde-protected microcapsules of fish oil (1.5% and 3.0% of DM, respectively). Unprotected and protected supplements contained, respectively, 74% and 58% of DM as lipids. Cows given the unprotected supplement reduced their feed intake by > 25%. Consequently, these cows lost body weight and produced less milk. DM intake, body weight, and milk yield were unaffected by protected fish oil. Fish oil reduced both milk fat and protein percentages, and decreased the proportion of short-chain fatty acids, stearic, and oleic acids in milk fat. Milk trans C18:1 fatty acids increased in cows given both unprotected and protected fish oil. Milk fat content of very-long-chain n3 polyunsaturated fatty acids, including C20:5 and C22:6, increased with fish oil in the diet. Accordingly, the peroxide index increased and a taste panel was able to detect unusual taste in milk from cows consuming the higher level of protected fish oil and disliked the milk from cows given unprotected fish oil. In conclusion, when lactating cows consumed fish oil, milk concentration of long-chain n3 fatty acids increased and mammary de novo synthesis of fatty acids decreased, but milk yield and milk protein content were reduced, and the milk was more susceptible to oxidation and its taste was adversely affected.
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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 | 0.001 | 0.001 |
| 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".