Performance of Timothy‐based Grass/Legume Mixtures in Cold Winter Region
Bibliographic record
Abstract
Abstract This study sought to identify grass/legume mixtures that increase the yield and persistence of forage stands with improved nutritive quality in cold‐winter regions, compared with the standard mixture of timothy (Phleum pratense L.)/red clover (Trifolium pratense L.)/alsike clover (Trifolium hybridum L.). Timothy was mixed with either perennial ryegrass (Lolium perenne L.), meadow fescue (Festuca pratensis L.) or Kentucky bluegrass (Poa pratensis L.). The legumes in mixtures were red clover, alfalfa (Medicago sativa L.) or white clover (Trifolium repens L.). Averaged over three production years, the majority of mixtures had greater dry matter (DM) yields than the standard (8.35 t ha−1). Timothy, grown alone and in three mixtures, outyielded the standard by 19–30 %. Yield reductions in mixtures over the 3‐year period were greatest with red clover, and least with bluegrass. Mixtures with alfalfa were highest in nitrogen (28.4 g kg−1), while grasses grown alone (24.6 g kg−1) and the standard mixture (25.1 g kg−1) were the lowest in N. Mixtures with red clover or alfalfa had the least neutral detergent fibre (NDF), averaging 418 and 429 g kg−1 respectively. Mixtures including white clover were initially low in NDF at 347 g kg−1 in year 1 but increased to 550 g kg−1 in year 3 as white clover composition declined in the sward.
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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 | 0.001 | 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".