The Germination Responses of Five Bermudagrass Cultivars to Seedcoating and Temperature
Bibliographic record
Abstract
As turfgrass quality of seeded bermudagrass (SB) [ Cynodon dactylon (L.) Pers.] cultivars has increased over the past 20 years, so has their use. Improved SB cultivars offer ease of establishment and convenience of storage while providing an economic advantage over vegetative propagation. Currently, most improved seeded cultivars are marketed with a seedcoating unique to each seed company. However, germination of some of the new cultivars is not ideal. The objectives of this study were to determine commercial coating effects on germination, compare germination among cultivars, and evaluate the effect of temperature on germination of five bermudagrass cultivars. ‘Princess-77’, ‘Riviera’, ‘Transcontinental’, and ‘Yukon’ were selected for a series of 21-day germination studies with ‘Arizona Common’ included as a standard cultivar. The study compared two seed lots of coated and uncoated samples of the five cultivars for germination response to six temperature regimes. Cumulative count intervals occurred on Day 7, Day 14, and Day 21. Overall, commercial seedcoating did not significantly affect SB germination. However, both temperature regime and cultivar were significant factors. Germination percentage was greatest with either the 35/25 °C or the 30/20 °C temperature regimes. ‘Riviera’ exhibited the lowest overall germination, whereas ‘Transcontinental’ and ‘Arizona Common’ exhibited the highest.
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) | 0.000 | 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".