(51) Intermountain West Native and Adapted Grass Species and Their Management for Turfgrass Applications
Bibliographic record
Abstract
The ongoing drought in the Intermountain West has brought a great deal of attention to water conservation over the past several years. During that time, turfgrass irrigation has been targeted as a source for large potential water savings. Some communities promote downsizing turfgrass areas as the best water conservation measure. In reality, turfgrass controls erosion, reduces evaporation from a site, and provides a safe surface for human activities. One alternative to elimination would be wider use of low water-use-grasses appropriate to the area. However, many questions arise regarding the choice of such grasses and their management. Our research addresses these questions. Plots have been established at Montana State University, Bozeman; Utah State University, Logan; and USDA-NRCS Plant Materials Center, Bridger, Mo. The grasses considered include 12 single species and 12 mixed species stands of `Cody' buffalograss, `Foothills' Canada bluegrass, `Bad River' blue grama, sheep fescue, sandberg bluegrass, muttongrass, and wheatgrasses `Sodar' streambank, `Road Crest' crested, `Rosana' western, and `Critana' thickspike with Kentucky bluegrass and tall fescue as controls. Line source irrigation allowed the plots to be evaluated at a number of levels of irrigation. Experimental measurements on the plots included growth response as determined by clipping yield and quality ratings, and species composition. Fescues and wheatgrasses retained their color, texture, and density throughout the growing season, regardless of moisture level. Warm-season grasses performed well in June, July, and August only, and worked poorly in mixtures as the green cool-season grasses could not mask the brown dormant leaves in cooler weather.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".