RUMINANT NUTRITION SYMPOSIUM: Acidosis: New insights into the persistent problem1
Bibliographic record
Abstract
The Ruminant Nutrition Symposium titled "Acidosis: New insights into the persistent problem" was held at the Joint Annual Meeting of the American Dairy Science Association, American Society of Animal Science, Poultry Science Association, Asociación Mexicana de Producción Animal, Western Section-ASAS, and the Canadian Society of Animal Science in Denver, Colorado, July 11 to 15, 2010. The objective of the symposium was to provide the ruminant nutrition community with new insights and perspectives from recent research findings on acidosis. Under modern production systems, ruminants are fed high-grain diets to maximize their energy intake and productivity. However, feeding highly fermentable diets often causes excess fermentation and results in accumulation of fermentation acids in the rumen, leading to a decrease in feed intake, poor feed efficiency, liver abscesses, and lameness in feedlot cattle or lactating dairy cows. Although our understanding of nutritional factors (i.e., effects of type and processing method of grains and importance of physically effective fiber) affecting rumen pH have increased substantially over the past few decades, rumen acidosis has continued to be a common problem in the ruminant livestock industry. The symposium program was organized to review recent research findings in acidosis with more emphasis on physiological aspects, and provide novel insights into the persistent problem.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".