Effects of feeding a high- or moderate-starch prepartum diet to cows on newborn dairy heifer calf responses to intravenous glucose tolerance tests early in life
Bibliographic record
Abstract
The objective of this study was to evaluate the effect of feeding a prepartum diet with a high or moderate starch content on growth and insulin sensitivity of female offspring early in life. Thirty-eight Holstein heifer calves were born to dams fed either a high-starch (26% starch on a DM basis, HI; n = 20) or moderate-starch (14% starch on a DM basis, MOD; n = 18) prepartum diet commencing at 28 ± 3 d before expected parturition date. Following birth, all calves were housed individually and fed three 2-L meals of colostrum within the first 24 h of life and offered 10 L/d of milk replacer (26% CP, 18% fat, mixed to 130 g/L). Body weight of calves was measured at birth and on d 2 (after colostrum feeding but before milk feeding), 10 ± 2, and 20 ± 2. A glucose tolerance test was performed at a minimum of 6 h after their last colostrum or milk meal to evaluate insulin sensitivity on d 2, 10 ± 2 and 20 ± 2. Body weight did not differ throughout between HI and MOD calves; however, calves born to primiparous dams were smaller compared with those born to multiparous dams. Glucose or insulin concentrations were not different before the glucose tolerance test. Following the glucose tolerance test, maximum glucose concentrations were not different between treatments at any time point. However, HI calves had greater insulin area under the curve, and HI calves had greater maximum insulin concentrations on d 2. Glucose or insulin clearance rates were not different nor was the calculated insulin sensitivity index between treatments. These findings suggest that feeding a HI prepartum diet may reduce some insulin sensitivity indicators of female offspring early in life.
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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 | 0.001 |
| 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".