Short communication: Effects of dietary fat supplements and forage:concentrate ratio on feed intake, feeding, and chewing behavior of Holstein dairy cows
Bibliographic record
Abstract
Feed intake and feeding behavior of dairy cows fed diets that varied in fat supplementation and forage:concentrate (F:C) ratio were investigated. Eight multiparous Holstein dairy cows were used in a replicated 4x4 Latin square experiment with 21-d periods. Treatments were 1) no supplemental fat and 34:66 F:C ratio; 2) 2% hydrogenated palm oil and 34:66 F:C ratio; 3) 2% yellow grease and 34:66 F:C ratio; and 4) 2% yellow grease and 45:55 F:C ratio. Cows were fed ad libitum twice daily as total mixed ration with free access to water. Dry matter intake (DMI) was not affected by fat supplementation regardless of fat source, whereas increased F:C ratio (from 34:66 to 45:55) lowered DMI by 7.5%. Meal interval, eating rate, and meal size were lower for cows fed yellow grease, and eating rate was less for cows fed the 45:55 F:C ratio diet. Chewing activity was not affected by fat supplementation, but was greater for cows fed the 45:55 F:C ratio diet. Results suggest that supplementation of 2% hydrogenated palm oil or 2% yellow grease had little effect on DMI and chewing behavior of Holstein dairy cows fed a 34:66 F:C ratio diet. The 2 fat sources can replace each other, depending on the availability or cost. Results also showed that DMI and chewing activity can be effectively manipulated by changing the F:C ratio of 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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".