White Prairie Clover (Dalea candida Michx. ex Willd.) and Purple Prairie Clover (Dalea purpurea Vent.) in Binary Mixtures with Grass Species
Bibliographic record
Abstract
Native forage legumes may have potential for summer/fall grazing in semiarid prairie regions in mixture with grasses. The objective of this study was to evaluate two native clovers in binary mixtures with the introduced grasses when harvested in July and September to simulate late summer or fall stockpile forage. Eight binary clover–grass mixtures were seeded in a split-plot design with 4 replications at Swift Current, Saskatchewan, Canada. Mixtures included (i) AC Antelope white prairie clover (WPC)-Admiral meadow bromegrass (MBG), (ii) WPC-AC Success hybrid bromegrass (HBG), (iii) WPC-Bozoisky Russian wildrye (RWR), (iv) WPC-TomRWR, (v) AC Lamour purple prairie clover (PPC)-AdmiralMBG, (vi) PPC-AC SuccessHBG, (vii) PPC-BozoiskyRWR, and (viii) PPC-TomRWR. Clover establishment differed (p = 0.03) in July where WPC had 77.8% greater proportion in mixture than PPC, although both clovers increased (p < 0.001) in September to similar legume proportions, 663.2 and 876.1 kg/ha, respectively. Clovers with bromegrasses produced 41.9% more forage dry matter yield in summer than clovers with Russian wildryes (p < 0.001), though the latter mixtures had slightly better nutritive value (avg. 7.0% vs. 5.2% crude protein (CP). Clover–MBG exhibited higher (53.6%) in vitro organic matter digestibility (IVOMD) than Clover–HBG (51.2%) (p = 0.04). Purple prairie clover with grass or both clovers in mixture with bromegrasses, produced adequate forage biomass for summer and fall grazing, except clovers with Bozoisky RWR, while clovers with both RWR cultivars had acceptable forage nutritive value for summer in this semiarid prairie region.
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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".