Agronomic Behaviour of Some Cynodon Dactylon Ecotypes for Turfgrass Use in the Mediterranean Climate
Bibliographic record
Abstract
In Italy, the expansion of turfgrasses is limited by the lack of suitable species for cultivation in the Mediterranean climate. With this view, Mi.Te.A.Med. (Turfgrass improvement in the Mediterranean climate) research project was developed with the main purpose to find out and agronomically characterise native turfgrass species of Southern and Central Italy and to compare them with some commercial cultivars. During the first step of the research, 11 sites from 6 regions of Southern and Central Italy were identified. In these sites 24 ecotypes of Cynodon dactylon L. (Pers.) were collected and their habitus, phenology plus some biometric parameters have been determined. During the two years of research both botanic and agronomic characterisation of the collected C. dactylon ecotypes and their comparison with 3 commercial cultivars (Panama, Transcontinental and Yukon) was carried out. In the first year the colour loss interval was assessed. In the second year, colour index was measured by an electronic colorimeter, weekly growth rate was measured by a turfmeter, turf quality and ground cover percentage were assessed by visual estimate. Some native accessions showed behaviour similar to commercial cultivars while an ecotype from the Abruzzo region showed better results compared to the commercial cultivars for several quality indices.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".