Nutritional and Metabolic Characteristics of <i>Brassica carinata</i> Co-products from Biofuel Processing in Dairy Cows
Bibliographic record
Abstract
The increased utilization of Brassica carinata in the biofuel industry in Canada has resulted in the large-scale production of co-products that can be potentially exploited as alternative protein ingredients in dairy ration. The objectives of this study were to investigate the nutritive value of carinata presscake and meal for dairy cows in terms of (1) nutrient and antinutrient composition, (2) rumen degradation kinetics of organic matter (OM), crude protein (CP), and neutral detergent fiber, (3) hourly effective degradation ratio and potential N to energy synchronization, (4) intestinal digestion of rumen undegraded protein (RUP), and (5) total metabolizable protein (MP) supply to the small intestine. Samples (n = 3) of carinata meal, carinata presscake, and canola meal (as reference feed), collected from three consecutive batches, were evaluated. In comparison to canola meal, carinata presscake and meal had greater (p < 0.05) contents of CP [39.7 versus 48.5 and 53.5% dry matter (DM)], with a high proportion of soluble crude protein (24.0 versus 53.0 and 72.6% CP), resulting in their extensive degradation (59.2 versus 76.3 and 89.3% CP) in the rumen. As a result, carinata presscake and meal supplied smaller (p < 0.05) quantities (92 and 136 g/kg of DM) of MP compared to canola meal (153 g/kg of DM). The contents of glucosinolates were greater (p < 0.05) in carinata presscake (168.5 μmol/g) and meal (115.2 μmol/g) compared to canola meal (3.4 μmol/g), limiting its utilization as a ruminant feed. Carinata co-products can be used as an alternative feed protein source, given their nutrient composition, rumen degradation, and intestinal digestion characteristics, provided that the high glucosinolate content can be reduced.
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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 | 0.001 | 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) | 0.000 | 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".