Effects of Alfalfa Particle Size on Ensalivation Rate, Chewing Efficiency, and Functional Specific Gravity of Particulate Matter in Hereford Steers
Bibliographic record
Abstract
Six ruminally fistulated Hereford steers (body weight=414±13 kg) were used in a switch back design to determine whether two particle sizes of alfalfa hay (18.75 and 4.65 mm theoretical cut length) influenced salivary secretion during eating. The experiment carried out in two 26-d periods, with 11-d of adaptation to ration, followed by 5 d for determining the level of voluntary feed intake, 7-d for adaptation to feeding to 90% of voluntary feed intake, and 3-d for measurements. Saliva secretion was measured during the morning meal by rumen evacuation technique at 35 minute after feeding through the rumen fistula of each steer. Coarse and fine alfalfa had the same chemical composition. The geometric mean of coarse and fine alfalfa particles during the eating time decreased by 40.62 and 45.53%, respectively. Reduction of particle size increased the functional specific gravity of alfalfa hay and particulate matter. In addition, the gas associated with particles had similar trend to the functional specific gravity. Production of saliva in milliliters (P=0.001), per kg of dry matter intake (P<0.0001) and per each kilogram of neutral detergent fiber (NDF) intake (P<0.0001) were affected by alfalfa particle size. Saliva production was higher in coarse alfalfa treatment. Reduction of alfalfa particle size reduces the ability of alfalfa forage as a physically effective fiber source in feeding of ruminant by decreasing the ability to saliva secretion.
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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 | 0.000 | 0.000 |
| 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".