79 Effect of source and dietary concentration of trace minerals on serum mineral status, ruminal pH, and ruminal volatile fatty acids in lactating Holstein dairy cows
Bibliographic record
Abstract
Abstract Inorganic sources of trace minerals are commonly supplemented in dairy cow diets; however, there has been an increase in the supplementation of minerals complexed with organic compounds. These organic trace minerals are thought to have greater bioavailability which may enhance rumen fermentation and absorption. The objective of this study was to assess the effects of dietary concentration and source of supplemental trace minerals on serum trace mineral status and rumen fermentation. Six lactating Holstein cows were used in a 6 x 6 Latin square design with a 23-day adaptation and 5-day experimental period. Cows were fed the same basal diet daily except for the difference in source [organic (ORG) versus inorganic (INO)] and concentration (50%, 100%, and 200% based on NRC recommendations) of trace mineral supplemented. During the experimental period feed intake and blood were collected daily. Rumen fluid was collected on the final two days of the experimental period. Data was analyzed with PROC MIXED in SAS 9.4. Dry matter intake (18.1 ± 0.70 kg), serum mineral concentrations (Cu, Mn, Se, and Zn), and rumen pH (6.5 ± 0.64) did not differ among the treatments. However, serum concentration of Co was higher in 200% ORG compared to 50% and 100% INO and 50% ORG. Ruminal concentration of acetate was higher in 50% and 100% ORG compared to 200% ORG. Butyrate ruminal concentration was higher at 50% ORG compared to 200% ORG. Ruminal propionate concentration was higher in 50% INO and 50% ORG compared to 100% INO and 200% ORG. These findings demonstrate serum trace mineral status and ruminal pH are not tightly controlled by the source of trace minerals when supplemented at 50%, 100% and 200% of the NRC recommendations, however rumen fermentation may be affected by the dietary concentration of trace minerals in the diet.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".