PSXV-24 Dietary strategies to reduce the impact of high-concentrate diet on performance, ruminal fermentation and milk composition of dairy goats.
Bibliographic record
Abstract
This study investigated the use of dietary lipid supplements as a strategy to prevent or reduce the intensity of milk fat depression in goats fed high-concentrate diets. Thirty Alpine goats were housed in pens with Calan gate feeders at kidding, and received a total mixed ration with a forage-to-concentrate ratio (F:C) of 55:45 on a DM basis (27,4% of NDF and 17,9% of starch) during a pretrial period (23 ± 5 d). Data from the last 4 d of the pretrial period were used as covariates (d 0). Goats were blocked by milk fat concentration and randomly assigned to 1 of 3 diets containing a F:C ratio of 45:55 (25,3% of NDF and 19,4% of starch) for 41 d. Diets were: 1) Control (CTRL; no supplemental fat); 2) a high-palmitic acid diet (PALM; 16:0 at 1.9 % of DM; and 3) a high 18:3 n-3 diet (FLAX; 18:3 n-3 at 1.5% of DM). Data were collected on d 10, 20 and 41 and analyzed in a mixed model with repeated measures. In the CTRL group, feeding the high-concentrate diet reduced milk fat concentration from 4,34 to 3,53% (d 0 and 41, respectively; P< 0.01). PALM feeding increased fat yield on d 10 (12%) and 41 (21%) relative to CTRL and on d41 relative to FLAX (16%; all P< 0.05). No treatment effect was observed on ruminal pH (6,32; P >0.93) and individual volatile fatty acids (P>0,12). However, the concentration of ruminal propionate increased from 17.9 to 20.3%, while acetate and the acetate: propionate ratio decreased respectively from 68.1 to 62.0% and from 4.21 to 3.19 (P< 0.01) on d 41, relative to d 0. Feeding PALM can alleviate the reduction of milk fat synthesis in goats receiving high concentrate diets in early lactation. Key Words:
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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 | 0.001 | 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".