Processing effects on the starch and fibre composition of Canadian pulses
Bibliographic record
Abstract
Starch and fibre contribute to the energy components and add functionality to the end-product feed ingredients. An understanding of the impact of processing on carbohydrate content will support accurate formulation of feed. Six ingredients, grown or sourced in Canada, were used in this study. They included five pulses, Amarillo peas, Dun peas, chickpeas, lentils, and faba beans, and soybean meal (SBM) as a comparison. All ingredients were ground into fine or coarse products and then pelleted at one of three different temperatures. Grinding reduced the total starch (TS) content of Amarillo peas and chickpeas ( P < 0.05), crude fibre (CF) in Dun peas ( P < 0.05), and total dietary fibre (TDF) and insoluble fibre (IDF) in lentils ( P < 0.05). Grinding only affected soluble fibre (SDF) in chickpeas. The effect of pelleting was variable for TDF across pulses. Pelleting did not affect the SDF content of pulses ( P > 0.05). Finely processed SBM had higher ( P < 0.05) TS, TDF, and IDF content than coarsely processed SBM. Results indicate that grinding and pelleting could affect the starch and fibre composition of some pulses.
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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 | 0.000 | 0.001 |
| 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".