Bibliographic record
Abstract
Lawn is the heart of the garden and the centre for social life. These are considered as an essential component of private gardens, public landscapes and parks in various parts of the world. Lawn is the best foreground to enjoy the charm and beauty of the ornamental plants and features. They are developed for aesthetic pleasure, as well as for sports or other outdoor recreational purpose. Based on climatic requirements, they are classified into two categories, viz. cool season and summer season grasses. In the review, description, varieties, importance and uses of Alkali grass ( Puccinella distans ), Annual Blue Grass ( Poa annua ), Canada blue grass ( Poa compressa ), Colonial Bentgrass ( Agrotis capillaris ), Creeping Bentgrass, ( Agrotis stolonifera ), Fine Fescues, Kentucky Blue Grass ( Poa pratensis ), Perennial Rye Grass ( Lolium perenne), Rough Blue Grass ( Poa trivialis ), Velvet bentgrass ( Agrotis canina ) and Wheatgrass ( Agropyron spp ) and warm season grasses namely Bahia grass ( Paspalum notatum ), Bermuda grass ( Cynodon dactylon ), Blue Grama ( Bouteloua gracilis ), Buffalo grass ( Buchloe dactyloides ), Carpet grass ( Axonopus affinis), Centipede grass ( Eremochloa phiuroides ), Kikuyu grass ( Pennisetum clandestinum ), Salt grass ( Distichlis spicata) and Zoysia grass ( Zoysia spp.) are discussed in details.
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 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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".