Implication of Modified Chemical Profiles of Different Seed Proteins through Heat-Related Processing to Protein Nutrition and Metabolic Characteristics in Ruminant Systems
Bibliographic record
Abstract
The objectives of this study were to reveal the implication of modified chemical profiles of protein in cool-season-adapted cereal seeds through heat-related processing to protein nutrition and metabolic characteristics in ruminant systems. The parameters of protein-related chemical and nutritive profiles modified by heat-related processing included (a) chemical and nutrient profiles, (b) degradation kinetics ( K d and BCP), (c) digestion (IDP and TDP), (d) degraded protein balance (DPB/OEB value), and (e) metabolizable protein (MP, DVE, and FMV values). The seeds used in this study included cool-season-adapted wheat (CSW), triticale (CST), and corn (CSC). Each grain source had 3 consecutive year-replicated samples and were subjected to different heat-related processing: dry heating (DH) and moist heating (MH). The results showed that (1) the heat-related processing significantly modified metabolic characteristics of protein in the seeds ( p < 0.05), (2) in comparison to DH, MH had a dramatic improvement ( p < 0.05) in protein utilization profiles (decreased rumen degradation and increased intestinal digestion), (3) the seeds had significant ( p < 0.05) difference in rumen degradation and intestinal digestion, (4) among the seeds, the CSW had the highest milk value (FMV; p < 0.05) and was increased by MH application ( p < 0.05), and (5) the results showed that the seeds responded independently to different heat-related processing. MH-related processing had a more profound impact on CSW and CST in chemical profiles and nutrition. The CSC had less response to the heat-related processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".