Performance of White Clover Cultivars in Mixtures with Orchardgrass in Atlantic Canada
Bibliographic record
Abstract
The climate in the Atlantic Provinces of Canada is ideally suited to pasture production, with up to seven months of growing season in some areas. Moreover, many of the soils are too shallow to support deep-rooted and highly valued forages such as alfalfa (Medicago sativa L.) and corn (Zea mays L.). However, the main limiting factor to pasture production occurs in spring where there are frequent freeze-thaw cycles, which reduce the dependability of snow cover for insulation, and this results in winter injury (Dzikowski et al., 1984). The interest in white clover (Trifolium repens L.) as a pasture species comes from the fact that naturalised forms of white clover are widespread and dominate old grazed pastures in the Atlantic Provinces of Canada. Naturalized ecotypes of white clover have been shown to differ in seasonal patterns of DM yield in Nova Scotia. However these ecotypes, although winter hardy, arc not as productive in the short term compared to bred cultivars (Fraser, 1988). The only recommended cultivar in Atlantic Canada is Sacramento ladino, but its usage is limited to areas where the risk of winter kill is slight (Fraser, I 986). This paper presents selected results from field trials comparing cultivars of white clover, alfalfa and red clover (Trifolium pratense L.) grown in mixtures with orchardgrass (Dactylis glomerata L.).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".