Interactions Between Trace Elements and Macro Minerals in Pregnant Heifers
Bibliographic record
Abstract
There is a lack of information on mineral interactions that take place during a heifer’s pregnancy when nutrient demand is high. The objective of the present study was to evaluate the interactions between the macro minerals calcium (Ca), phosphorus (P), magnesium (Mg), sulphur (S), sodium (Na) and potassium (K) and the trace elements copper (Cu), iron (Fe), manganese (Mn), molybdenum (Mo), selenium (Se), zinc (Zn) and cobalt (Co) in pregnant heifers. Twenty-four pregnant heifers (age 18.2 ± 0.14 months; 87.5 ± 4.74 days in gestation; 497 ± 8.5 kg of body weight) were used in a 7 d digestibility trial (indirect method using lignin as a marker) during which they were fed a diet that provided minerals in accordance with NASEM requirements for gestating beef cattle. At the end of the digestibility trial, blood (jugular venipuncture) and liver (biopsy) samples were collected from each heifer. Multiple linear regression models were developed based on the main correlations and by considering collinearity effects between variables. Iron intake positively affected Mo concentration in serum, which was >10 µg/dL for most of the animals. Apparent absorption of Fe was negatively affected by Mo intake and Ca to P ratio in feed, whereas Se absorption was negatively affected by Fe intake and positively by Mo intake. The absorption coefficients of Mo and Fe were inversely related, emphasizing the interactions between these elements. Serum Fe and Cu in the liver were positively correlated, while liver Fe was inversely related to liver Mn. Fewer interactions were observed between the macro minerals. In conclusion, within the interactions observed, those between Fe and Mo deserve special attention, as high dietary Fe is commonly found in typical ruminant diets, and high serum Mo may indicate molybdenosis. Furthermore, high Fe intake appears to impact Se and Mn utilization.
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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) | 0.000 | 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".