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Record W2289933087 · doi:10.17012/entac2014.333

Potenciais para o uso da geotermia na arquitetura brasileira

2015· article· pt· W2289933087 on OpenAlexaboutno aff

Bibliographic record

VenueIRIS Research product catalog (Sapienza University of Rome) · 2015
Typearticle
Languagept
FieldEnvironmental Science
TopicUrban Arborization and Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.004
Scholarly communication0.0000.001
Open science0.0020.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.006

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.

Opus teacher head0.089
GPT teacher head0.303
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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