Urban Green Spaces Restoration Using Native Forbs, Site Preparation and Soil Amendments—A Case Study
Bibliographic record
Abstract
Restoration of urban green spaces with native flora is especially important for promoting various ecosystem services. Although there have been years of research on land reclamation, ecological restoration and plant establishment, there is a lack of knowledge on how to reintegrate the native ecological component, specifically forb species in urban green spaces. We evaluated the restoration potential of 24 native forbs using different site preparation (herbicide, tillage, herbicide with tillage and control) and soil amendment (100% compost, 50% compost with 50% topsoil, 20% compost with 80% topsoil and control) treatments in a recreational park in Edmonton, Alberta, Canada. Soil texture and nutrients generally increased with increased compost application rate; some declined within a year, others increased. Based on survival and growth analysis, the forb species with the highest potential for use in urban green spaces were Penstemon procerus, Fragaria virginiana, Heuchera cylindrica, Agastache foeniculum, Antennaria microphylla, Mentha arvensis and Geum aleppicum. Native forb species response was more prominent with soil amendment than site preparation. Treatments with greater amounts of compost had greater survival, growth, species richness, cover and noxious weed cover than control treatments. This study suggests amendment of soil with compost can positively influence forb species restoration in urban green spaces; under some conditions site preparation may be required.
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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 | 0.001 |
| Science and technology studies | 0.002 | 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 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".