Effect of Combinations of Fish Meal and Feather Meal on Milk Fatty Acid Content and Nitrogen Utilization in Dairy Cows
Bibliographic record
Abstract
The effect of supplemental fishmeal in combination with feathermeal at two different proportions in the diet on milk docosahexaenoic acid (DHA) content was investigated. Recently, benefits to human health have been attributed to the consumption of this fatty acid, which is normally present in marine lipids. Six Holstein cows past peak lactation were used in a Latin square design with a 2 x 3 factorial arrangement of treatments. Fish- and feathermeals were prepared as pellets at 4:1 and 1:4 combinations and offered at 3.75, 11.75, and 27% of the diet. The supplements were top-dressed onto a basal diet based on corn silage that was progressively replaced by supplement. Nitrogen balance measures were made during the experiment because of the wide range in crude protein content of experimental diets. Milk protein content increased with level of supplementation in the diet reflecting the protein quality of the supplements used. There was overall higher milk DHA content when cows consumed the supplement containing more fishmeal than feather meal. Milk DHA content increased in a quadratic fashion, as more of either supplement was included in the diet. Apparent transfer efficiency of DHA from diet to milk declined with increasing amount of DHA in the diet. Results from this experiment suggest that transfer of docosahexaenoic acid from diet to milk may depend on diet composition and quantity present in the diet.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".