Effects of starch concentration of close-up diets on rumen pH and plasma metabolite responses of dairy cows to grain challenges after calving
Bibliographic record
Abstract
The objective of this study was to evaluate the effects of starch concentration of close-up diets on plasma concentrations of energy metabolites and rumen pH of dairy cows after calving. Eighteen multiparous Holstein dairy cows (mean parity = 2.78; mean body weight = 708 kg; mean body condition score = 3.08) fitted with ruminal cannulas were assigned to treatment balanced for parity, body condition score, and expected calving date. Cows were enrolled in the study at d 28 ± 3 before the expected calving date and fed either a low-starch (LS; 14.0% starch) or high-starch (HS; 26.1% starch) diet until parturition. All cows were fed a common diet after calving (25.1% starch). A grain challenge was performed on d 7 ± 2 and 21 ± 2 after calving by dosing 6.35 kg (dry matter basis) of finely ground barley and wheat grain (1:1) into the rumen via cannula. Feeding the HS diet before calving increased the duration (369 vs. 49 min/d) and area of pH below 5.8 (85.1 vs. 5.2 pH × min/d) during d -10 to -8. In addition, even though all cows were fed a common diet after calving, HS cows tended to have longer duration (177 vs. 76 min/6 h) and greater area of pH below 5.8 (67.8 vs. 20.3 pH × min/6 h) during a grain challenge on d 7. Plasma concentration of insulin tended to be greater in cows fed the HS diet (1.40 vs. 1.09 ng/mL), whereas plasma free fatty acid concentration was not different between treatments during the grain challenge on d 7. During the grain challenge on d 21, neither rumen pH nor blood metabolites were different between the HS and LS cows. These findings suggested that feeding an HS diet during the close-up period does not mitigate rumen pH depression but may exacerbate it after calving compared with feeding an LS diet.
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".