Green-up of Seeded Bermudagrass Cultivars as Influenced by Spring Scalping
Bibliographic record
Abstract
Winter dormancy is the main impediment to a wide acceptance of warm-season turfgrasses in the Mediterranean countries of Europe due to a loss of color during the winter months. Scalping during late winter or early spring has been recommended anecdotally to enhance spring green-up of bermudagrass ( Cynodon dactylon ); however, information is lacking on the effectiveness of this practice. A study was conducted to investigate the effects of spring scalping on spring green-up of eight bermudagrass cultivars (Barbados, Contessa, La Paloma, Mohawk, NuMex Sahara, Princess-77, SR 9554, and Yukon) grown in a transition zone environment. The trial was carried out in Spring of 2009 and 2010 on plots established in July 2005 at the experimental farm of the University of Padova (northeastern Italy). Half of the plots for each cultivar were subjected to spring scalping, which was applied in both years on 13 Mar. with a rotary mower set at a height of 28 mm. Soil temperatures were recorded hourly during the research period at a depth of 2.5 cm. The percentage of green cover was estimated weekly from 0 to 98 days after spring scalping (DASS). Soil temperatures in scalped plots were greater than in unscalped plots. Among the cultivars tested, ‘Yukon’ showed earliest spring green-up, with no difference between the scalping treatments, reaching 80% green cover by the end of April. For all other cultivars, scalped plots reached 80% green cover 10 to 18 days earlier than unscalped plots. Results showed that scalping enhanced spring green-up, primarily for cultivars that recover slowly from winter dormancy.
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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".