Drought Stress Responses of Some Prairie Landscape C4 Grass Species for Xeric Urban Applications
Bibliographic record
Abstract
Creating xeric landscapes in lawns and prairies is a significant challenge and practical need in arid urban environments. This study examined the drought resistance of some C4 grass species for constructing urban lawns and prairies. A factorial experiment based on randomized complete block designs with four replications was conducted. Experimental treatments were two irrigation levels (100% and 50% Field Capacity (FC)) and five warm-season grass species (Andropogon gerardii Vitman, Sorghastrum nutans (L.) Nash, Panicum virgatum L., Schizachyrium scoparium (Michx.) Nash, and Bouteloua curtipendula (Michx.) Torr.). The effects of drought on physiological, morphological, and qualitative characteristics of the grass species were analyzed. Drought conditions induced a decrease in all the measured traits. However, fewer physiological, morphological, and qualitative characteristics were affected by drought stress on Andropogon gerardii, Schizachyrium scoparium, and Bouteloua curtipendula, compared to the other two species. Overall, warm-season grasses of Andropogon gerardii, Schizachyrium scoparium, and Bouteloua curtipendula, had greater adaptability to drought stress, making them promising C4 grass species for prairie or lawn landscaping in arid urban environments. Landscape professionals and decision-makers should consider using Andropogon gerardii, Schizachyrium scoparium, and Bouteloua curtipendula, as these were the most resilient grass species for drought-tolerant prairie landscaping schemes. Sorghastrum nutans and Panicum virgatum may be used as a second priority if a more diverse variety of grasses is required for drought-resilient prairie or lawn landscaping in arid cities.
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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".