Effects of moist heat treatment on ruminal nutrient degradability of sunflower seed
Bibliographic record
Abstract
Abstract A study was conducted to determine the effects of moist heat treatment (autoclaving at 127 °C with a steam pressure of 117 kPa for 10, 20 and 30 min) of sunflower seed on crude protein (CP) fractions, ruminal dry matter (DM) and CP degradabilities, and ruminal disappearance of amino and fatty acids. Two ruminally fistulated cows were used in a randomised complete block design. Heating of sunflower seed decreased soluble protein and increased neutral detergent‐insoluble protein, with little effect on acid detergent‐insoluble protein. Results of the in sacco study showed that moist heat treatment decreased (cubic effect, P < 0.05) ruminal degradability of DM and CP of sunflower seed. Ruminal undegraded CP of raw sunflower seed was low (84 g kg−1 of CP) and increased (cubic effect, P < 0.05) by 139, 143 and 164% as the heating time increased from 0 to 10, 20 and 30 min respectively. Ruminal disappearance of all amino acids (following 12 h of incubation) was greater (P < 0.05) for raw than for heated sunflower seed. Similar results were also observed for ruminal disappearance of fatty acids. It was concluded that moist heat treatment decreased ruminal DM and CP degradability of sunflower seed (by 17 and 19% respectively) and thus increased the concentrations of amino acids and polyunsaturated fatty acids available for digestion in the small intestine. Copyright © 2003 Society of Chemical Industry
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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 | 0.000 |
| 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) | 0.001 | 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".