Análise das causas raízes que dificultam a adoção de telhados verdes nas edificações brasileiras com utilização da metodologia Delphi
Bibliographic record
Abstract
Green roof systems are considered a sustainable practice to mitigate the adverse effects of urbanization in densely populated areas. Green roofs mitigate urban heat islands, retain rainwater and generate peak flow and runoff, improve urban air quality, absorb noise losses, increase the thermal efficiency of buildings and provide a pleasing aesthetic effect as buildings. Germany, France, UK, Hong Kong, USA, Canada, Australia, Singapore, Japan and other countries are encouraging the installation of green roofs during the construction of new buildings and adapting the old ones so that this technique becomes a reality in the near future. However, the use of this type of coverage in developing countries and regions is still not widespread. The objective of this research is to identify root causes that hinder the adoption of green roofs in Brazilian buildings. Understanding the deep barriers is important to promote the implementation of green roofs on a large scale and consequently, to achieve the benefits of their installation. This research was developed through a review of technical literature and field research (questionnaire) with experts using the Delphi Methodology approach. The essential results are that the main barriers to the adoption of green roofs in Brazilian buildings are associated with problems of knowledge and knowledge of the technology and that there are barriers associated with all stages of the building's life cycle, including the planning phases and design, construction and operation and management.The abstract should contain similar information than in "resumo" and must be written in english. Avoid using automatic translation.
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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 | 0.027 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".