The Relative Merit of Ruminal Undegradable Protein from Soybean Meal or Soluble Fiber from Beet Pulp to Improve Nitrogen Utilization in Dairy Cows
Bibliographic record
Abstract
Early lactating dairy cows were used to determine whether the replacement of solvent-extracted soybean meal [SSBM; a source of rumen-degradable protein (RDP)] with expeller soybean meal (ESBM; a source of rumen-undegradable protein), or the replacement of high-moisture shelled corn (HMSC) with beet pulp (a source of soluble fiber) would be effective in improving efficiency of N usage for milk production. The study was designed as a replicated 4 x 4 Latin square with 21-d periods. Eight multiparous Holstein cows were fed, ad libitum, the following diets, which were based on alfalfa silage and HMSC, and formulated to be isocaloric: 1) basal diet without a protein supplement (negative control diet: NC); 2) NC supplemented with solvent-extracted SBM (diet SSBM); 3) NC supplemented with expeller SBM (diet ESBM); 4) SSBM in which unmolassed dried beet pulp replaced half of the HMSC (diet SSBMBP). Compared with diet NC, protein supplementation increased intake of organic matter and dry matter. Milk and milk protein yields were lower with NC but this diet resulted in the greatest efficiency of N usage for milk production (30% milk N/N intake). Supplementation with ESBM, a proven source of RUP, increased plasma concentrations of histidine and branched-chain amino acids, and reduced milk urea N concentration, but failed to improve the yields of milk or milk protein. Milk fat yield tended to decrease with RUP supplementation. Replacing part of HMSC with soluble fiber from beet pulp (SSBMBP) tended to decrease milk production compared with SSBM; the effect was due to a reduction in dry matter intake. There were no differences among diets SSBM, ESBM, or SSBMBP in urinary excretion of purine derivatives. Neither substitution of ESBM for SSBM nor partial replacement of HMSC with beet pulp altered the efficiency of N usage for milk production or manure N excretion.
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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".