Effect of Concentrate Level and Feeding Management on Chewing Activities, Saliva Production, and Ruminal pH of Lactating Dairy Cows
Bibliographic record
Abstract
Eight ruminally cannulated lactating Holstein cows were used in a double 4 x 4 Latin square to determine the effects of 1) proportion of barley silage [40, 50, and 60% of dry matter (DM)] in the diet, and 2) feeding a total mixed ration (TMR) compared with separate ingredients (SI) on chewing activities, saliva production, and ruminal pH. Although cows fed SI were offered a diet containing 50% silage, they actually consumed a diet containing 43% silage (DM basis). Dry matter intake and milk yield were similar for all diets (18.2 kg of DM/d and 27.2 kg/d, respectively). Cows fed the 40% silage TMR spent more time eating than cows fed SI (243 vs. 198 min/d), but rumination time was similar (546 min/d). Eating time was similar among the TMR diets, but rumination time increased from 498 to 516 and 584 min/d as silage in the TMR increased from 40 to 50, and then to 60%, respectively. The secretion of saliva per gram of feed was 4.43, 3.18, and 1.19 ml/g of DM with consumption of silage, TMR, and concentrate, respectively. Resting salivation rate was similar for all diets (101 ml/min). Regardless of the diet, cows secreted 239 +/- 17 L/d of saliva, and ruminal pH was below 5.8 for 10 h/d. Results indicated increased chewing time did not increase total daily saliva secretion because increased eating and ruminating saliva was associated with decreased resting saliva. Feeding SI increased the risk of acidosis, because cows ate a higher proportion of concentrate than intended.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".