PSXIII-12 Characterization of Physiochemical and Nutrient Profiles of Feedstock and co-Products from Canola bio-oil Processing in Ruminants: Impacted by Source Origin
Bibliographic record
Abstract
Abstract The objective of this study was to characterize physiochemical and nutrient profiles of feedstock and co-products from canola processing that were impacted by source origin to compare feedstocks and co-products (mash and pellet) from five different bio-oil processing plants with five batches of samples in each processing plant in Canada (CA) and in China (CH) collected by the Canola Council of Canada (CCC). The detailed physicochemical and nutrients parameters for ruminants were determined in chemical composition, TDN and truly digestible nutrients, energy profile (ME, NE), and protein and carbohydrate subfractions and their degradation and digestion in dairy cows. The data were analyzed using the Mixed model procedure in SAS 9.4 with RCBD. The treatment differences were compared using Tukey method. The results showed CP was greater in CH meals (P=0.003). The EE was not different between CA and CH (P >0.05). TDN1x was similar in canola meals regardless of the country (P >0.05). CH meals and feedstock had greater tdCP and tdNDF than CA (P< 0.05), while CA had greater tdNFC (P< 0.05). The energy values of ME3x, NELp3x, NEm3x, and NEg3x were similar in canola meals from both countries (P >0.05). No differences were observed in the energy profile of feedstock between CA and CH (P >0.05). The results also showed that pelleting affected the protein fractionation of CA canola meals (P< 0.05). Canola meals were different between CA and CH in the soluble (PA2) and slowly degradable fractions (PB2) (P< 0.05). The carbohydrate fractions of soluble fiber (CB2), digestible fiber (CB3), and indigestible fiber (CC) were different among CH meals (P< 0.05). CH presented greater water-soluble carbohydrate (CA4, P=0.04) and less CB2 (P=0.01) and CC (P< 0.001) than CA canola meals. Although the seeds were similar within and between counties, the oil-extraction process and pelleting seemed to have generated some different aspects on the meals in both countries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".