Bibliographic record
Abstract
There is increasing public pressure on the turfgrass industry to reduce maintenance inputs. Grasses native to North America seem perfect candidates for low-maintenance turfgrass as they have evolved under the environmental extremes of North America. The objectives of this research were to identify native grass species suitable for use in drought, saline and low-maintenance conditions. Fifteen native grass species for a total of 31 entries were evaluated: alpine bluegrass ('Poa alpina' L.), alkali grass (' Puccinellia nuttaliana' [Schultes] Hitchc.), blue grama (' Bouteloua gracilis' [Willd. ex kunth] Lag. ex Griffiths), buffalograss ('Buchloe dactyloides' [Nutt.] Engelm.), Canada bluegrass (' Poa compressa' L.), fescue spp., fowl bluegrass ('Poa palustris ' L.), Idaho bentgrass ('Agrostis iddaensis'), inland desert saltgrass ('Distichlis stricta' [Torr.] Rydb.), marsh muhly ('Muhlenbergia racemose' [Michx.] B.S.P.), prairie junegrass ('Koeleria macrantha' [Ledeb.] LA. Schultes), rough hairgrass ('Agrostis scabra' Willd.), side-oats grama ('Bouteloua curtipendula' [Michx.] Torr.), sweetgrass ('Hierchloe odorata ' [L] Beauv.) and tufted hairgrass ('Deschampsia caespitosa ' [L.] Beauv.). The entries with high quality ratings for low-maintenance turf use were: Minnesota ecotype blue grama, 'Bismarck' and 'Sharp's Imp. II' buffalograss, inland desert saltgrass, 'Bad River' blue grama, 'Barkoel' prairie junegrass and 'Golfstar' Idaho bentgrass. The last four were also moderately tolerant of saline soil conditions. The warm season grasses entries blue grama and buffalograss were extremely drought tolerant, maintaining consistent green colour. Most entries will require a breeding and selection program before being released to the public for low-maintenance turfgrass use. This research provides useful information on a number of native grass species suitable as low-maintenance turf and the relevant drought and saline tolerance of many native grass species.
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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) | 0.002 | 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".