The Effects of Trace Mineral Source, Diet Type, and Monensin and Tylosin on Rumen Fermentation Characteristics in Beef Cattle
Bibliographic record
Abstract
Sixteen Angus crossbred steers were used to determine the impact of trace mineral (TM) source, diet type, and monensin and tylosin supplementation on rumen fermentation characteristics. Experiment 1: Steers were adapted to a finishing diet before being assigned dietary treatments. A 2 × 2 factorial arrangement of treatments was utilized with factors: (1) TM source (sulfate; STM or hydroxy; HTM) and (2) with or without monensin and tylosin (MT). Following the 28-day feeding period, rumen samples were collected at 0, 2, and 4 h post-feeding. Experiment 2: Steers were adapted to a lactating dairy cow diet and assigned dietary treatments for 28 days. A 2 × 2 factorial arrangement of treatments was utilized. Factors included (1) TM source (STM or HTM) and (2) with or without monensin (M). On day 29, rumen samples were collected at 0, 2, and 4 h post-feeding. Rumen fluid samples from both experiments were analyzed for short chain fatty acids (SCFA), pH, and ammonia concentrations. Experiment 1: Molar proportions of acetate were lesser (P < 0.04) and propionate greater (P < 0.01) in steers receiving HTM compared to steers receiving STM. Experiment 2: Total SCFA production was greater (P < 0.01) in steers supplemented with HTM compared to STM. Supplementation of M reduced the molar proportion (P < 0.01) of acetate and increased (P < 0.05) molar proportions of propionate and butyrate. These data indicate that TM source and M may modulate rumen fermentation characteristics but their impacts on rumen fermentation may be diet dependent.
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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) | 0.000 | 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 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".