Characterization of physiochemical and nutrient profiles in canola feedstocks and co-products from bio-oil processing: impacted by source origin
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to characterize physiochemical and nutrient profiles of feedstock and co-products from canola bio-oil processing that were impacted by source origin. The feedstocks and co-products (mash, pellet) were randomly collected from five different bio-oil processing plants with five different batches of samples in each bio-processing plant in Canada (CA) and China (CH). METHODS: The detailed chemical composition, energy profile, total digestible nutrient (TDN), protein and carbohydrate subfractions, and their degradation and digestion (CNCPS6.5) were determined. RESULTS: The results showed that TDN1x was similar in meals between CA and CH. CH meals and feedstock had higher, truly digestible crude protein (tdCP) and neutral detergent fiber (tdNDF) than CA while CA had higher truly digestible non-fiber carbohydrate (tdNFC). The metabolizable energy (ME3x), net energy (NELp3x, NEm3x, and NEg3x) were similar in meals between CA and CH. No differences were observed in energy profile of seeds between CA and CH. The protein and carbohydrate subfractions of seeds within CH were similar. The results also showed that pelleting of meals affected protein sub-fractionation of CA meals, except rapidly degradable fractions (PB1), rumen degradable (RDPB1) and undegrdable PB1 (RUPB1), and intestinal digestible PB1 (DIGPB1). Canola meals were different in the soluble (PA2) and slowly degradable fractions (PB2) between CA and CH. The carbohydrate fractions of intermediately degradable fraction (CB2), slowly degradable fraction (CB3), and undegradable fraction (CC) were different among CH meals. CH presented higher soluble carbohydrate (CA4) and lower CB2, and CC than CA meals. CONCLUSION: The results indicated that although the seeds were similar within and between CA and CH, either oil-extraction process or meal pelleting seemed to have generated significantly different aspects in physiochemical and nutrient profiles in the meals. Nutritionists and producers need to regularly check nutritional value of meal mash and pellets for precision feeding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".