Análise das causas raízes que dificultam a adoção de telhados verdes nas edificações brasileiras com utilização da metodologia Delphi
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 it