Ruminal metabolism, blood parameters and animal behavior of bulls submitted to sub-acute ruminal acidosis (SARA) receiving different buffers in high-concentrate diets
Bibliographic record
Abstract
This study aimed to evaluate the supplementation of four different buffers into a high-grain diet on ruminal fermentation, blood metabolites, and feeding behavior of feedlot cattle. Five rumen-cannulated bulls (492 ± 24 kg) were distributed in a 5 x 5 Latin square design, through the treatments: CONT (no buffer), RUMO, RUMF, BICA and ALGA. The buffers did not alter (p > 0.05) the apparent digestibility of nutrients, ruminal fluid pH, volatile fatty acid profile, and acetic/propionic ratio compared to the CONT. Except for propionic and isovaleric acids, which were different (p < 0.05) among treatments at 4 and zero hours after feeding, respectively. Ruminal lactic acid accumulation was greater (p < 0.05) in BICA, while ammoniacal nitrogen concentrations were highest (p < 0.05) in CONT and lowest in RUMF. Blood glucose and creatinine were unaffected (p > 0.05), whereas urea and lactate concentrations were reduced (p < 0.05) in RUMO. Among the enzymes, only gamma-glutamyl transferase and creatine kinase showed treatment effects (p < 0.05). Furthermore, feeding and drinking times were unaffected by treatments; however, rumination increased (p < 0.05) in BICA, and idleness was higher in CONT. Overall, buffer inclusion modified ruminal and metabolic responses, indicating a possible modulation of ruminal acidosis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".