Turfgrass Evaluation of Native Grasses for the Northern Great Plains Region
Bibliographic record
Abstract
ABSTRACT A range of native grass species evolved under the environmental extremes of a continental climate in the northern Great Plains, but most have not been evaluated for their suitability as turf. The objective of this research was to evaluate the turfgrass potential of a range of native grasses under three mowing heights. Twelve species (a total of 28 entries) were evaluated under three mowing heights (62, 38, and 18 mm), for turf quality, color, and density at two locations in Manitoba, Canada. The species evaluated included alpine bluegrass (Poa alpina L.), alkali grass [Puccinellia nuttalliana (Schult.) Hitchc.], alpine fescue [Festuca ovina L. var. brachyphylla (Schult. & Schult. f.) Piper, syn. F. brachyphylla Schult. & Schult. f.], blue grama [Bouteloua gracilis (Kunth) Lag. ex Griffiths], buffalograss {Buchloe dactyloides (Nutt.) Engelm. [= Bouteloua dactyloides (Nutt.) Columbus]} Canada bluegrass (Poa compressa L.), fescue spp., fowl bluegrass (Poa palustris L.), Idaho bentgrass (Agrostis idahoensis Nash), marsh muhly [Muhlenbergia racemosa (Michx.) Britton et al.], prairie junegrass [Koeleria macrantha (Ledeb.) Schult.], rough hairgrass {Agrostis scabra Willd. [= A. hyemalis var. scabra (Willd.) H. L. Blomq.]}, side‐oats grama [Bouteloua curtipendula (Michx.) Torr.], and tufted hairgrass [Deschampsia cespitosa (L.) P. Beauv.]. Entries showed similar results across all mowing heights. ‘Bad River’ blue grama, Minnesota ecotype blue grama, and ‘Barkoel’ prairie junegrass entries showed high quality ratings across all years and locations. Blue grama, a warm‐season grass, was drought tolerant and maintained consistent green color throughout the growing season. Although a number of the species showed promise, most entries will require a breeding and selection program before release as low‐maintenance turfgrasses.
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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 | 0.000 | 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".