Bibliographic record
Abstract
Oba, M. 2011. Review: Effects of feeding sugars on productivity of lactating dairy cows. Can. J. Anim. Sci. 91: 37–46. Sugars are water-soluble carbohydrates that are readily available in the rumen. Although sugars ferment faster than starch or fibre in the rumen, the rates of disaccharide hydrolysis and monosaccharide fermentation vary greatly depending on the type of sugar and rumen environment. Despite rapid fermentation in the rumen and their potential to provide greater fermentable energy to enhance microbial protein production, feeding sugars in place of dietary starch sources may not decrease rumen pH or improve N utilization efficiency and milk protein production in dairy cows. However, feeding high-sugar diets often increases dry matter intake, butyrate concentration in the rumen, and milk fat yield. These nutritional characteristics of sugars may allow us to use high-sugar feedstuffs as an alternative energy source for lactating dairy cows to increase dietary energy density with reduced risk of rumen acidosis, but there is little evidence in the literature to indicate that the synchrony of rumen fermentation would be enhanced by feeding high-sugar diets with high soluble protein. Greater butyrate production from feeding high-sugar diets is expected to enhance proliferation of gut tissues, but its physiological mechanisms and effects of butyrate metabolism on overall productivity of dairy cows warrant further investigations.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".