Energy to Protein Ratio of Grass–Legume Binary Mixtures under Frequent Clipping
Bibliographic record
Abstract
Forages with a greater energy to protein ratio increase dairy cow N use efficiency. We studied binary mixtures of one legume and one grass species that can increase the ratio of energy availability to protein degradability under frequent clipping. Timothy ( Phleum pratense L.), Kentucky bluegrass ( Poa pratensis L.), tall fescue [ Schedonorus phoenix (Scop.) Holub], orchardgrass ( Dactylis glomerata L.), meadow bromegrass ( Bromus biebersteinii Roem. & Schult.), and meadow fescue ( Festuca elatior auct. Amer.) were seeded with either alfalfa ( Medicago sativa L.), white clover ( Trifolium repens L.), or birdsfoot trefoil ( Lotus corniculatus L.). Carbohydrate and protein fractions (Cornell Net Carbohydrate and Protein System), other nutritive attributes, and dry matter (DM) yield were determined at the first two harvests of the first production year at two sites in eastern Canada. Alfalfa mixtures had a greater ratio of water‐soluble carbohydrates (WSC) to crude protein (CP) and of readily degradable carbohydrate fractions to readily degradable protein fractions than the average of all mixtures; however, they had lower digestibility of neutral detergent fiber and DM, greater fiber concentration, and similar yield. Mixtures with meadow fescue and tall fescue had a greater WSC/CP ratio, yield, and fiber concentration than the average of all mixtures. Mixtures of meadow fescue with any legume species, especially with alfalfa, provided the best combination of a high ratio of WSC/CP (0.70), high yield, and average digestibility. The feasibility of maintaining this desired composition throughout the growing season and for several cropping years remains to be determined.
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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) | 0.001 | 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".