A comparative study of fodder Galega (<i>Galega orientalis</i> LAM.) and common forage legumes in monoculture and grass–legume binary mixture in Canadian Prairies
Bibliographic record
Abstract
Fodder Galega ( Galega orientalis Lam.) is a perennial legume adapted to the temperate regions of the world. The objective of this research was to compare the performance of fodder Galega to alfalfa ( Medicago sativa L.), sainfoin ( Onobrychis viciifolia Scop.), and cicer milkvetch ( Astragalus cicer L.) in monocultures and grass–legume mixtures. From 2018 to 2020, a multiple-location trial was conducted at Swift Current, Saskatoon, Melfort, SK and Beaverlodge, AB, Canada. The average forage mass of fodder Galega in monoculture was 3226, 1176, and 1678 kg·ha–1 at Melfort, Saskatoon, and Swift Current, respectively, and was lower than alfalfa. However, fodder Galega and alfalfa had similar forage mass at Beaverlodge (7900 and 7670 kg·ha−1, respectively). The proportion of fodder Galega in the grass–legume mixtures was 36%–42% in 2019, decreasing to 3%–27% at Saskatoon. At Swift Current, fodder Galega in the mixtures was 11%–13% in 2019, which almost disappeared from the stand in 2020. At Beaverlodge, fodder Galega maintained 38%–47% in 2019 and increased to 47%–69% in 2020. Fodder Galega had similar acid detergent fiber to cicer milkvetch at two of four sites, which was lower than those of alfalfa and sainfoin. The crude protein of fodder Galega and its mixtures with grasses was lower than other mixtures at the three Saskatchewan sites, but was higher at Beaverlodge. Our results indicate that fodder Galega has potential to be utilized as a forage legume in cooler northern regions, but its productivity was low in the Dark Brown and Brown soil.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".