Introduction of four native legumes of the Canadian Prairies in monoculture and in mixtures with meadow bromegrass (<i>Bromus riparius</i>)
Bibliographic record
Abstract
Many questions remain concerning the viability and productivity of seeding native legumes in the Canadian Prairies for forage production. Field research was conducted with four native legume species (Astragalus flexuosus, Dalea purpurea, Hedysarum boreale, and Vicia americana) to evaluate performance in Swift Current and Saskatoon, SK. The experimental design was a randomized complete block design with four replicates to evaluate legume–grass mixtures and monoculture performance, botanical composition, and effect of harvest dates (July and August) from 2016 to 2018. The native legume–grass mixtures performed differently at the sites, with greater foliar cover at Saskatoon but a greater proportion of legumes in mixtures at Swift Current. The mixtures had similar forage nutritive value as monoculture Bromus riparius, with legumes contributing 10% or less of the forage dry matter yield (DMY) at both sites. Astragalus flexuosus showed the greatest foliar cover and produced the greatest DMY in monoculture at both sites. Based on this study, native legumes would need to make up a larger proportion of forage dry matter yield to change the nutritional value of mixtures. In a subsequent seeding rate evaluation, the four legume species were planted at three seeding rates [300, 200, and 100 pure live seeds (PLS) per metre] and tested for DMY one year following establishment near Swift Current. Increasing seeding rates up to 300 PLS·m−1 corresponded with an increase in seedling density and foliar cover, but DMY was not affected. Additional research with A. flexuosus is needed to demonstrate its value as a forage.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".