Manipulating Characteristics of High Straw Dry Cow Diets to Improve Consistency in Intake Across the Transition Period
Bibliographic record
Abstract
The aim of this thesis research was to determine if reducing the length of wheat straw and water addition to high straw dry cow diets could improve intake, reduce feed sorting, and improve metabolic health and production of dairy cows across the transition period. In 2 studies, Holstein cows were assigned to a dietary treatment at dry off and fed the same lactating diet for 28 d post-calving. In study 1, cows were fed a diet that had straw chopped with either a 2.54-cm screen, or a 10.16-cm screen. In study 2, cows were fed a diet that either had water added or had no water. The diet with straw chopped with a 2.54-cm screen and the diet with added water resulted in improved intake during the dry period and in the week leading up to calving. Cows sorted less against the long particles when fed the shorter chopped straw and the diet with added water. Lastly, cows fed the shorter chopped straw and cows fed the diet with added water, had improved rumen health in the week following calving. The results of these studies suggest that reducing the chop length of wheat straw and adding water can improve intake, reduce feed sorting, and promote both metabolic and rumen health.
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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".