Effect of supplementing glucose in drinking water on the energy and nitrogen status of the transition dairy cow
Bibliographic record
Abstract
The objective of this experiment was to examine the effects of supplementing the drinking water of transition dairy cattle with glucose on the energy and nitrogen status of the cow. Twenty-four multiparous Holstein dairy cows were randomly assigned to one of three treatments for an experimental period that extended from -7 to 21 d of lactation. The treatments included a 0% (control), 1% (10 g L-1) and 2% (20 g L-1) glucose solution metered into the drinking water of dairy cows. Water and dry matter intake (DMI) were not affected by treatment. Glucose supplementation in the drinking water significantly reduced the rumen fluid ammonia and serum urea concentrations. No differences were seen in serum concentrations of glucose, β-hydroxybutyrate (BHBA), non-esterified fatty acids (NEFA), and the energy corrected milk (ECM) yield. The calculated NE balance was not affected by glucose supplementation. The results of this trial demonstrate that glucose supplemented into the drinking water of transition cows improves the nitrogen status of the cow, but does not alter the NEL balance during the periparturient period. Key words: Water supplementation, glucose, transition cow, serum urea
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".