Compatibility of new sainfoin populations as forage mixtures with alfalfa and orchardgrass in Alberta
Bibliographic record
Abstract
Sainfoin is a highly nutritious non-bloating leguminous forage crop of temperate regions. Despite non-bloating properties, its use in pastures was limited due to low dry matter yield (DMY), lack of persistence in the mixed pasture, and slow regrowth. The paradigm then shifted when new sainfoin cultivars planted in mixtures with alfalfa reduced 98% bloat incidence in ruminants. Two experiments were conducted to determine the compatibility of sainfoin with grass and alfalfa. Experiment (Exp.) I was established by drilling alfalfa cv. Longview and orchardgrass cv. Kayak with new sainfoin populations in alternate or cross-seeding rows under irrigated and unirrigated conditions in Lethbridge, AB. Exp. II was established by drilling sainfoin populations in mixture with alfalfa cultivars in alternate rows under irrigation. DMY and botanical composition (dry matter basis) were observed for both experiments. In Exp. I, monoculture orchardgrass produced the least DMY compared with the mixtures under both growing conditions and over all growing seasons. Alfalfa–sainfoin mixture yielded higher under irrigatated and less under unirrigated conditions relative to alfalfa monoculture. The percentage DMY contribution of sainfoin in alfalfa–sainfoin mixtures decreased but increased in sainfoin–orchardgrass mixtures over successive harvests under both environments. In Exp. II, we observed that new sainfoin populations mixed with alfalfa cvs. Beaver and Longview yielded greater ( P < 0.05) than their corresponding monocultures and this increase was not associated with the proportion of the species in the mixture suggesting new sainfoin populations are as productive as alfalfa.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".