Pre-calving energy density and rumen protected lysine impacted blood metabolites and biomarkers of liver functions in dairy cows during the transition period.
Bibliographic record
Abstract
Abstract Dairy cow usual faces negative energy balance and disorder of normal organs function due to mismatch between energy intake and energy demands. Negative energy balance directly affects liver function and blood metabolites because of liver used as source of energy supply and center of metabolic activity. The study aimed to determine the effect of pre-calving energy density and rumen-protected lysine on blood metabolites and biomarkers of liver functions in dairy cows during the transition period. Forty 3rd lactation Holstein cows were randomly allocated to one of the four dietary treatments (High energy with rumen-protected lysine (HERPL) = 1.53NEL plus 40 g Lys, High energy without lysine (HECK) = 1.53NEL, Low energy with rumen-protected lysine (LERPL) = 1.37NEL plus 40 g Lys, and Low energy without lysine (LECK) = 1.37NEL arranged in a 2 x 2 factorial design. Blood samples were collected during the transition period and concentrations of blood metabolites and biomarkers of liver functions were measured. Interaction between pre-calving high energy diet and RPL tended to increase plasma albumin, numerically increased glucose, decreased TG, total bilirubin and AST concentrations. The result revealed that pre-calving high energy diet increased insulin, albumin and decreased blood urea nitrogen and total bilirubin concentrations and substantial favor liver functions during the transition period.
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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".