Preliminary Findings on the Correlation between Water-soluble Carbohydrate Content in Stolons and First Year Green-up of Seeded Bermudagrass Cultivars
Bibliographic record
Abstract
Warm-season grasses are not widely accepted in Mediterranean countries because they lose color during the winter months. A study was conducted at the University of Padova (Padova, Italy) to determine whether fall and spring water-soluble carbohydrate (WSC) content in stolons of seeded bermudagrass cultivars ( Cynodon dactylon ) influenced spring green-up in the first year of establishment. Nine bermudagrass cultivars (La Paloma, Mohawk, NuMex Sahara, Princess 77, Riviera, SR 9554, Barbados, Contessa, and Yukon) were seeded in July 2005, and dry weight and WSC content in stolons were measured in Fall 2005 and again in Spring 2006. The percentage of green cover and days needed to achieve 80% green cover (D80) were regressed against November and March values of stolon dry weight and WSC content to determine if they were good predictors of D80. ‘Yukon’ showed earliest spring green-up by end of April, and ‘Princess 77’ and ‘Riviera’ were slowest, needing 43 to 46 days more than ‘Yukon’ to reach D80. There was a significant inverse relationship between November (r 2 = 0.57) and March (r 2 = 0.77) WSC content in stolons and D80 for all nine bermudagrass cultivars. These results suggest that bermudagrass cultivars with high WSC in stolons recover more rapidly from dormancy during establishment than those with low WSC content.
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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 | 0.001 |
| 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".