Bermudagrass Cultivars Differ in Their Summer Traffic Tolerance and Ability to Maintain Green Turf Coverage Under Fall Traffic
Bibliographic record
Abstract
Bermudagrass [Cynodon spp. (L.) Rich.] is one of the most widely used turfgrass species for golf courses and sports fields in the southern United States and in the transitional climatic zone. Continuous trafficking from play or equipment can reduce bermudagrass coverage and turf quality. This study evaluated 42 bermudagrass cultivars for their response to traffic. Traffic was applied in summer and fall of 2007 and 2008 with a Cady Traffic Simulator. There were several commercially available cultivars that had the highest coverage in both summers when trafficked including Barbados, Celebration, Contessa, Dune, Midlawn, Mirage II, Panama, Premier, Princess 77, Patriot, Riviera, Southern Star, Sovereign, Sundevil II, Sunsport, TifGrand, Tifsport, Tifway, Transcontinental, Veracruz, and Yukon. However, only the cultivars Barbados, Celebration, Contesssa, and Premier and the experimental genotypes SWI‐1003, SWI‐1046, Tift No. 1, and Tift. No. 2 were in the top statistical grouping for green turf coverage in both summer and fall of both years. These results demonstrate that bermudagrass cultivars vary in their response to traffic. Selecting improved, traffic‐tolerant bermudagrasses will help reduce maintenance inputs and increase sustainability of golf courses and athletic fields while also producing a better‐quality and safer surface for sports.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".