Potenciais para o uso da geotermia na arquitetura brasileira
Bibliographic record
Abstract
This paper stems from an interest in the expansion of knowledge of environmental comfort and the constant search for ways to minimize emissions and energy consumption by buildings, aligned to the principles of sustainability in the construction sector. The initial results of ongoing research entitled "Usage of alternative and renewable energy sources in architecture" carried out at Group of Studies in Architecture, Environmental Comfort and Energy Efficiency FAU UFRJ in partnership with Università degli Studi di Roma La Sapienza in its line of research "Efficientamento energetico e bioclimatico degli edifici". The work aims to present the state of art about the use of geothermal energy in buildings, its potentiality and benefits for reducing emissions and energy consumption. This is a passive strategy for thermal comfort, which takes advantage of the temperature of the subsoil near the surface, through the use of underground air ducts for pre heating rooms in winter, or pre cool them in summer: a technology not much explored in Brazil, but with application examples in Italy, Germany, Canada, the U.S. and several other countries. So, it’s the result of an extensive bibliography exploratory research with a brief description of the technology and illustrative examples of its application in various climatic contexts, 538 highlighting the need for further exploration in national context to assist in passive conditioning in buildings, since the use of geothermal potential can contribute to reducing dependence on artificial systems for thermal comfort. The aim is to move forward the discussion on the non-use of this technology, nowadays attributed to the lack of available data of soil characteristics and to the high investments for its implementation. It is believed that the production of knowledge, stimulating demand, will contribute to future researches, investments and cost reductions.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".