Turf Quality and Freezing Tolerance of ‘Tifway’ Bermudagrass as Affected by Late‐Season Nitrogen and Trinexapac‐Ethyl
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bermudagrass [ Cynodon dactylon (L.) Pers.] is the most widely used species for intensively managed turf sites in the southern United States and in the transition zone. However, the lack of cold tolerance in many cultivars can result in significant winter injury. There is a limited body of information in the literature regarding management of bermudagrass to enhance cold tolerance, especially as it relates to N nutrition and the use of plant growth regulators (PGRs). As such, a 2‐yr field study (1998–1999 and 1999–2000) was conducted to examine the effects of late season N fertilization and trinexapac‐ethyl (TE) applications on morphology, quality, and freezing tolerance of ‘Tifway’ bermudagrass. During both years, monthly N applications were terminated on either 15 July, 15 August, or 15 September, while applications of TE were made on 15 August; 15 August and 15 September; or 15 August, 15 September, and 15 October. Late season applications of N and TE enhanced the fall green color retention of bermudagrass and promoted early spring green‐up (SGU). Neither N nor TE had a consistent effect on growth and development of bermudagrass rhizomes or stolons, and neither treatment had a consistent effect on the freeze tolerance of rhizomes. However, a positive attribute of these treatments is a significant increase in the overall green period of bermudagrass (20–25 d), which can prolong the playability of high maintenance sports facilities. From these studies we have concluded that, contrary to what is commonly believed, late season applications of N did not affect the freeze tolerance of bermudagrass rhizomes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it