The Salinity Tolerance of Seeded-type Common Bermudagrass Cultivars and Experimental Selections
Bibliographic record
Abstract
Turfgrass managers are using reclaimed water as an irrigation resource because of the decreasing availability and increasing cost of fresh water. Much attention, thereby, has been drawn to select salinity-tolerant turfgrass cultivars. An experiment was conducted to evaluate the relative salinity tolerance of 10 common bermudagrasses ( Cynodon dactylon ) under a controlled environment in a randomized complete block design with six replications. ‘SeaStar’ seashore paspalum ( Paspalum vaginatum ) was included in this study as a salinity-tolerant standard. All entries were tested under four salinity levels (1.5, 15, 30, and 45 dS·m −1 ) consecutively using subirrigation systems. The relative salinity tolerance among entries was determined by various parameters, including the normalized difference vegetation index (NDVI), percentage green cover determined by digital image analysis (DIA), leaf firing (LF), turf quality (TQ), shoot vertical growth (VG), and dark green color index (DGCI). Results indicated that salinity tolerance varied among entries. Except LF, all parameters decreased as the salinity levels of the irrigation water increased. ‘Princess 77’ and ‘Yukon’ provided the highest level of performance among the common bermudagrass entries at the 30 dS·m −1 salinity level. At 45 dS·m −1 , the percent green cover as measured using DIA varied from 4.97% to 16.11% among common bermudagrasses, where ‘SeaStar’ with a DIA of 22.92% was higher than all the common bermudagrass entries. The parameters LF, TQ, NDVI, DGCI, VG, and DIA were all correlated with one another. Leaf firing had the highest correlation with other parameters, which defined its value as a relative salinity tolerance measurement for common bermudagrass development and selection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".