PSXIII-12 Exploring nutritional differences of canola seeds and bio-processing co-products (meals, pellets) from different processing plants/companies in Canada and China for dairy cattle
Bibliographic record
Abstract
Abstract Canola was created as a low erucic acid and low glucosinolate seed, to produce high quality oil for human consumption and meal for use in livestock feed. China is an important user of Canadian canola products (seeds, oil, and meal). The extraction of the oil from the seed produces a co-product called canola meal. This meal is rich in protein and is used as a protein source in animal diets. However, differences in the characteristics of the seeds, or processing methods during oil extraction may affect the quality of this co-product. Plus, the synthesis of tissues and milk is related to the amino acids available to the animal for absorption in the small intestine. This study aimed to determine if there are significant differences in the intestinal digestibility (in vitro) of CP and DM between canola seeds and meals from different companies in Canada and to determine if there are significant differences between them in Canada and China. The three-step procedure was applied on residues from a 12-hour rumen incubation in fistulated dairy cows to estimate the intestinal digestibility of CP and DM. There were significant differences (P < 0.05) for TDDM (Total digestible dry matter) and IDP (intestinal digestibility of protein) of the meals between countries. The samples from China had higher TDDM (83.76% versus 81.53%, P = 0.018), while Canada’s had higher IDP (68.51% versus 65.28%, P = 0.016). No significant differences were observed within countries. Based on the material analyzed during this study, it is safe to affirm that there are no significant differences in the digestibility of DM and CP between Canada and China. It was concluded that the quality of the canola seeds or meals produced in both Canada and China were similar when used in dairy rations. Key words: canola seeds and bio-processing co-products (meals, pellets), nutritional differences, dairy cows
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".