59 Effect of heat processing on nutritive value of oat (Avena sativa L.) grain for dairy cattle as determined by DVE/OEB system and its relationship to oat grain protein molecular structure using advanced molecular spectroscopy.
Bibliographic record
Abstract
This study aimed to: (1) evaluate the heat processing effect on predicted nutrient supply to the small intestine (SI) in dairy cows; (2) detect associations between nutrient supply and protein molecular structure profiles of oat grain using Attenuated Total Reflectance Fourier transform molecular spectroscopy (ATR-FTIR). Oat grain was sampled from harvested plots (n=2) grown in 2014 and 2015. Each sample (1kg) was subsampled (4 portions) and each portion was subjected to one treatment: raw, dry heating (DH; air-draft oven 60min; 120°C); moist-heating (MH; autoclave at 1.05 kg/cmin situ trial to determine ruminal degradation kinetics. The DVE/OEB system was used to determine the degradability and digestibility of the nutrients, and rumen microbial protein synthesis. Results from the DVE/OEB system were correlated to protein molecular structure obtained using ATR-FTIR. Organic matter fermented in the rumen (FOM) and digested organic matter (DOM) were greater for DH and MIR (P0.05). High correlations were obtained between DVE and amide total area, amide I and II areas (AI; AII), amide I height (AIH), α-helix, β-sheet and α-helix:β-sheet, and between OEB and amide I:amide II ratio. In conclusion, heat processing improved the availability of OM in the rumen and CP in the SI from oat grain for dairy cows. The ATR-FTIR results could be used to predict nutrient supply in dairy cows.
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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 | 0.001 | 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".