Performance of multiple turfgrass species during prolonged heat stress and recovery in a controlled environment
Bibliographic record
Abstract
Abstract Increasing temperatures associated with climate change will be the next challenge for crop improvement, especially for turfgrass species that are often grown in urban green spaces. Previous research on turfgrass heat stress tolerance has been limited to a small number of species and cultivars with different heat stress conditions between studies; therefore, we assessed heat stress tolerance of 34 turfgrasses representing 14 species. Four replicates of each entry were established for at least 12 wk and then subjected to 49 d of heat stress (35/25 °C day/night) followed by a 4 wk recovery period (25/15 °C day/night). Turfgrass entries were assessed using the normalized difference vegetative index, the percentage of green obtained with digital image analysis, and membrane stability estimated by electrolyte leakage. Buffalograss [Bouteloua dactyloides (Nutt.) J.T. Columbus], Kentucky bluegrass (Poa pratensis L.), tall fescue [Schedonorus arundinaceus (Schreb.) Dumort], and slender creeping red fescue (Festuca rubra ssp. littoralis) were more tolerant of heat stress than the other species. The Canada bluegrass (Poa compressa L.), blue grama [Bouteloua gracilis (Willd. ex Kunth.) Lag. ex Griffiths], and smooth brome (Bromus inermis Leyss.) cultivars were all tolerant of heat stress; however, only one cultivar for these three species was tested, making species‐wide generalizations difficult. In some cases, the cultivars and/or selections within a given species differed in heat stress response. Almost all entries were able to recover from the heat stress by the end of the recovery period. Altogether, we were able to identify turfgrasses that should perform adequately in high‐temperature urban environments.
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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) | 0.001 | 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".