Early productivity and crude protein content of establishing forage swards composed of combinations of native grass and legume species in mixed-grassland ecoregions
Bibliographic record
Abstract
Mischkolz, J. M., Schellenberg, M. P. and Lamb, E. G. 2013. Early productivity and crude protein content of establishing forage swards composed of combinations of native grass and legume species in mixed-grassland ecoregions. Can. J. Plant Sci. 93: 445-454. We evaluated the early establishment productivity of forage swards of native, perennial, cool and warm season grasses, and legumes as they have the potential to provide non-invasive, productive, and drought resistant rangelands. Seven species with agronomic potential and a broad native geographic distribution were selected for testing including: nodding brome [Bromus anomalus (Coult.)], blue bunch wheatgrass [Pseudoregneria spicata (Pursh)], western wheatgrass [Pascopyrum smithii (Rydb.)], side oats grama [Bouteloua curtipendula (Michx.)], little blue stem [Schizachyrium scoparium (Michx.)], purple prairie clover [Dalea purpurea (Vent.)], and white prairie clover [Dalea candida (Willd.)]. Forage swards, including all seven monocultures, 21 two-species mixtures and a mixture with all species, were planted in two sites, Saskatoon and Swift Current, Saskatchewan. Western wheatgrass (WWG) had the highest overall plant density and the strongest effect on the forage yield of the forage swards; however, productivity and crude protein content were not reduced when other species were also included in the forage sward. Dalea spp. did not establish as well as the other species, but had the highest crude protein concentrations. This work provides insight into forage sward development at the establishment stage; additional work is required to determine long-term species impacts for well established forage swards.
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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 | 0.000 | 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".