Mixing warm-season turf species with red fescue ( <i>Festuca rubra</i> L. ssp. <i>rubra</i> ) in a transition zone environment
Bibliographic record
Abstract
Summary Turf managers and home lawn owners are often discouraged from using warm-season turf species in the transitional zones of Europe because they undergo dormancy during winter and lose colour up to five months. These issues may be addressed by mixing cool-season species together with warm-season turfgrasses. However, there is a lack of information on performances and dynamics of species succession in such turfgrass mixtures. Therefore, a two-year study was carried out at Padova University (Northeastern Italy) to test the effects of overseeding red fescue in bermudagrass and zoysiagrass cultivars on turf quality and species succession in the mixtures. In September 2011, red fescue 'Corail' (FRR) was overseeded at a 50 g m 2 rate on mature turfgrass plots of eight Cynodon dactylon (CD) cultivars and Zoysia japonica 'Companion' (ZJ). Monostands of CD 'Princess-77' and FRR were established for comparison purposes. Turf quality was estimated visually every other week and the frequencies of species in the mixtures were determined every month using a point-intercept method, by recording species in 10-cm segments of four lines of 1 m each. With the exception of summer, CD 'Princess-77' monostand had the lowest visual turf quality in comparison to all the mixtures tested. Significant differences were also observed among mixtures, with zoysiagrass-red fescue polystand showing the most important decline in turf quality from one year to the next. The two best performing mixtures (CD 'Contessa' + FRR and CD 'Yukon' + FRR) were characterized by high frequency of CD (i.e., average 82%) and intermediate frequency of FRR (i.e., average=82 88%). The results of this study suggest that the choice of species and cultivar plays a key role for establishing functional mixtures of warm-season grasses with red fescue in a transitional zone environment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".