New Research on Livestock Science and Dairy Farming
Bibliographic record
Abstract
Preface Prebiosis With Fermentable Carbohydrates in Pig Nutrition: A Holistic Approach to Productivity Digestibility and Nitrogen Balance of Non Conventional Protein Sources Fed to Pigs of Two Different Genotypes Effects of botanicals rich in polyphenolics on animal productivity and health. A Review. Milk Fatty Acid Profiles as Novel Nutritional and Environmental Management Tools: Scopes and Challenges The Greenhouse Gas Budget of the Dairy Industry in Canada Suitability of Simplified Milk Recording Methods for Genetic Evaluations Using Test-Day Models in Dairy Sheep Current Status of Composition and Somatic Cell Count in Milk of Goats Enrolled in Dairy Herd Improvement Program in the United States Gastrointestinal Nematode Effects on Grazing Dairy Herds During Development and Its Impact on Production Monitoring in Infectious Disease Status of Dairy Cattle Herds in Uruguay: A Random Effects Hierarchical Model Analysis Driving Forces Underlying Farmer's Decisions to Develop or Exit Dairy Production: Six Case Studies From Sweden Restructing Livestock Farms under Oil Boom in Transition Country: Case Study of Mangistau Oblast, Kazakhstan Livestock Mandatory Price Reporting A New Approach to Modelling UK Milk Production Under Quota Restriction Index.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.018 |
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".