Short Communication: Salivary Secretion During Meals in Lactating Dairy Cattle
Bibliographic record
Abstract
Four multiparous Holstein cows in midlactation were used in a 4 x 4 Latin square to evaluate whether source of forage influenced salivary secretion during eating in lactating dairy cows. The forages were allocated separately from the pelleted concentrates. Cows were offered 1 of 4 forages each period: barley silage, alfalfa silage, long-stemmed alfalfa hay, or chopped barley straw. Saliva secretion was measured during the morning meal by collecting masticates through the rumen cannula at the cardia of each cow. Rate of salivation (213 g/min) was not affected by forage source. However, the forage sources differed in eating rate (g of DM/min), which led to differences in ensalivation of forages (g of saliva/g of DM and g of saliva/g of NDF). On the basis of DM, ensalivation (g of saliva/g of DM) was greatest for straw (7.23) and similar for barley silage, alfalfa silage, and alfalfa hay (4.15, 3.40, and 4.34 g/g of DM, respectively). Higher ensalivation of straw could be accounted for by its higher neutral detergent fiber (NDF) content; ensalivation of NDF (g of saliva/g of NDF) was actually greatest for long-stemmed alfalfa hay (12.4) and similar for the other chopped forages (8.9). Cows consumed concentrate about 3 to 12 times faster than the various forages (DM basis), and ensalivation of concentrate was much lower (1.12 g of saliva/g of DM) than for forages. Feed characteristics such as particle size, DM, and NDF content affect salivary output during eating by affecting the eating rate. Slower eating rate and greater time spent eating may help prevent ruminal acidosis by increasing the total daily salivary secretion in dairy cows.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".