Differences in splanchnic metabolism between late gestation and early lactation dairy cows
Bibliographic record
Abstract
In the transition from the pre- to postcalving state, the demands on the cow increase from support of gestation to high rates of milk production. This extra demand is met partly by increased intake but may also involve altered metabolism of major nutrients. Six multiparous Holstein cows were used to monitor changes in net fluxes of nutrients across the portal-drained viscera and liver (splanchnic tissues) between late gestation and early lactation. Blood samples were obtained simultaneously from the portal, hepatic, and subcutaneous abdominal veins and the caudal aorta 18 d before expected calving and 21 or 42 d after calving. On the day of blood sampling and the 3 d preceding sampling, cows were fed every 2 h. The precalving (1.63 Mcal of net energy for lactation/kg and 1,326 g of metabolizable protein/d) and postcalving (1.72 Mcal of net energy for lactation/kg and 2,136 g of metabolizable protein/d) diets were based on corn silage, alfalfa hay, and corn grain. Dry matter intake increased postcalving. Net splanchnic release of glucose increased postpartum because of tendencies for both increased portal absorption and net liver release. Increased removal of lactate, rather than AA, contributed to the additional hepatic gluconeogenesis. Although portal absorption of AA increased with intake at the onset of lactation, hepatic removal of total AA-N tended to decline. This clearly indicates that liver removal of AA is not linked to portal absorption. Furthermore, net liver removal relative to total liver inflow even decreased for Gly, His, Met, Phe, and Tyr. Together, these data indicate that in early lactation, metabolic priority is given to direct AA toward milk protein production rather than gluconeogenesis, in cows fed a corn-based ration.
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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 | 0.001 | 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 | 0.000 |
| 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".