Canola meal replacing distillers grains with solubles for lactating dairy cows
Bibliographic record
Abstract
A study was conducted to determine the response to feeding diets containing canola meal (CM) as a protein supplement in place of all or portions of dried distillers grains with solubles (DDGS). Twelve lactating Holstein cows (4 primiparous and 8 multiparous) were fed in a 4 x 4 Latin square design over 4-wk periods. Data were collected wk 3 and 4 of each period. Diets were formulated in which CM was 100, 66, 33, and 0% of the supplemental protein replacing the protein from DDGS. All diets (averaged 15.1% crude protein and 4.5% ether extract) contained 55% forage and 45% concentrate, with the forage being 50% corn silage and 50% alfalfa hay. Dry matter intake (25.4 kg/d) was similar for all diets. Milk production (35.2, 35.8, 34.5, and 34.3 kg/d, respectively, for 100, 66, 33, and 0% CM) was similar for all diets, but tended to be greater with higher proportions of CM. Milk protein concentration (3.04%), fat concentration (3.92%), and fat yield (1.37 kg/d) were similar for all diets, whereas protein yield (1.08, 1.10, 1.05, and 1.03 kg/d, respectively, for 100, 66, 33, and 0% CM) tended to be greater with increasing amounts of CM in the diet. Feed efficiency (1.46 kg of energy-corrected milk/kg of dry matter intake) was similar for all diets. Lysine was the first limiting amino acid for milk protein synthesis when CM or DDGS were fed, whereas methionine was first limiting when the combination diets were fed. Concentrations of ammonia and volatile fatty acids in ruminal contents were similar for all diets. Canola meal is a suitable replacement for DDGS in dairy cow diets.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".