Effect of Thermal Processing on Estimated Metabolizable Protein Supply to Dairy Cattle from Camelina Seeds: Relationship with Protein Molecular Structural Changes
Bibliographic record
Abstract
This study evaluated the effect of thermal processing on the estimated metabolizable protein (MP) supply to dairy cattle from camelina seeds (Camelina sativa L. Crantz) and determined the relationship between heat-induced changes in protein molecular structural characteristics and the MP supply. Seeds from two camelina varieties were sampled in two consecutive years and were either kept raw or were heated in an autoclave (moist heating) or in an air-draft oven (dry heating) at 120 °C for 1 h. The MP supply to dairy cattle was modeled by three commonly used protein evaluation systems. The protein molecular structures were analyzed by Fourier transform/infrared-attenuated total reflectance molecular spectroscopy. The results showed that both the dry and moist heating increased the contents of truly absorbable rumen-undegraded protein (ARUP) and total MP and decreased the degraded protein balance (DPB). However, the moist-heated camelina seeds had a significantly higher (P < 0.05) content of ARUP and total MP and a significantly lower (P < 0.05) content of DPB than did the dry-heated camelina seeds. The regression equations showed that intensities of the protein molecular structural bands can be used to estimate the contents of ARUP, MP, and DPB with high accuracy (R(2) > 0.70). These results show that protein molecular structural characteristics can be used to rapidly assess the MP supply to dairy cattle from raw and heat-treated camelina seeds.
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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) | 0.000 | 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".