The effects of grinding and pelleting on nutrient composition of Canadian pulses
Bibliographic record
Abstract
Understanding the effects of processing pulses is required for their effective incorporation into livestock feed. To determine the impact of processing, Canadian peas, lentils, chickpeas, and faba beans, plus soybean meal (SBM; as a comparison), were ground into fine and coarse products and pelleted at three different temperatures (60–65, 70–75, and 80–85 °C). Grinding increased crude protein content in all the pulses ( P < 0.05), but did not affect most amino acids (AA) ( P > 0.05). Pelleting increased crude protein content in Amarillo peas, Dun peas, and lentils ( P < 0.05), but decreased in SBM ( P < 0.05). Pelleting increased cysteine, lysine (Lys), and methionine, and decreased histidine and tyrosine (Tyr) in most pulses ( P < 0.05). Comparatively, pelleting significantly increased Lys and decreased Tyr content in SBM ( P < 0.05). These results suggest that processing can positively affect protein and AA content of pulses. However, specific effects on nutritional composition differed across ingredient type.
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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 | 0.001 | 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".