Effects of Dietary Protein Solubility on Nitrogen Losses from Lactating Dairy Cows and Comparison with Predictions from the Cornell Net Carbohydrate and Protein System
Bibliographic record
Abstract
This experiment determined the effects of dietary protein solubility on amount, form, and route of nitrogen loss in lactating Holstein dairy cows, and the ability of the Cornell Net Carbohydrate and Protein System (CNCPS) to accurately predict rumen microbial yield, serum urea N (SUN), milk urea N (MUN), and fecal N. Eighteen multiparous Holstein cows were assigned randomly to one of three dietary treatments that were similar in crude protein (17.7%) content but differed in their content of soluble intake protein (SIP). Dietary contents of SIP, as % of total CP were 30, 36, and 48%. The experimental period was 21 d, and total N balance collections were done during the last 5 d. As dietary content of SIP increased, excretion of urinary N increased quadratically, and it was the primary route of N excretion. Urinary excretion of purine derivatives (PD) responded quadratically as dietary SIP content increased. The CNCPS predicted a quadratic decrease in total metabolizable protein (MP) supply. No effect of dietary content of SIP was detected on MUN and SUN. The CNCPS predicted a quadratic decrease in SUN and MUN as dietary SIP increased. Results from this study indicated that changing the dietary content of SIP altered routes of N excretion in dairy cows, but had no effect on total N balance. The CNCPS did not adequately predict changes in SUN and MUN for cows fed diets varying in SIP.
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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 | 0.001 | 0.002 |
| 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".