Green Roof Performance Towards Good Habitat for Butterflies in the Compact City
Bibliographic record
Abstract
Urban ecology is threatened by habitat loss and fragmentation due to increasing urbanization. Green roofs may act as habitats to compensate for loss of green space at the ground level. Here, we assessed greening variables of 11 green roofs for butterflies in Taipei City. Butterfly number, species, and richness on green roofs were lower than parks, but some less common species were observed on green roofs. The nectar plant area, number of nectar plant species and age of green roof were the main positive effectors of butterfly number. The height above ground of green roof had not impact on butterfly survival; supposed high rise buildings are spreading out over city that would be a potential good habitat for butterflies on skyscape. However, since the scale of green roofs was small in Taipei, we adopted a habitat suitability index (HSI) method to determine optimal value of selected greening variables to attract more butterfly number on green roofs. HSI curves' findings suggested that achieving a nectar plant area of more than 25 m2 and not less than 10 nectar plant species would greatly benefit butterfly number; meanwhile, the age of green roof was higher than 38 months, butterfly number is expected to increase rapidly. We confirmed that carefully design green roof could play a good habitat for butterflies in Taipei city.
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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".