The potential of legume-shrub mixtures for optimum forage production in southwestern Saskatchewan: A greenhouse study
Bibliographic record
Abstract
Grazing in fall and early winter decreases the cost of beef production in southwe stern Saskatchewan. This grazing system can be improved by utilizing legume and native shrub species, which exhibit high nutritive value in the fall. To realize the system's full potential, a better understanding of optimum mixtures of legumes and shrubs is required. A greenhouse study was conducted to optimize mixtures of legumes and shrubs for economical pasture production. The first goal was to obtain better understanding of synergy from mixtures of legumes and native shrubs. The second goal was to estimate the changes in soil quality caused by growing legumes and shrubs in monocultures or mixtures. Legume species studied were: alfalfa (Medicago sativa L.) (Alf), purple prairie clover [Petalostemon purpureum (Vert.) Rydb] (Pr Cl) and American vetch (Vicia americana Muhl.) (Vetch); shrubs were: winterfat [Krascheninnkovia lanata (Pursh) Guldenstaedt] (Wf) and Gardner's saltbush [Atriplex gardneri (Moq.) D. Dietr.] (Sb). Treatments consisted of five monocultures, six mixtures and a control. Data on plant biomass, forage quality and soil quality parameters indicate that legume and shrub mixtures of Alf + Wf and/or Alf + Sb can potentially provide diversified forage sources, extended grazing periods and higher or similar yields with enhanced or similar forage quality than when grown separately. Key words: Legume, native shrub, forage production, forage quality, winterfat, saltbush
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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 | 0.001 | 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".