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Record W4230341126 · doi:10.11159/icsect20.148

Social Housing and its Pathological Manifastations: a Case Study inPiumhi, Brazil

2020· article· en· W4230341126 on OpenAlexvenueno aff
Tobias Ribeiro Ferreira, Humberto Coelho de Melo, Stella Maria Gomes Tomé, Fernando da Costa Barros

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Development and Societal Issues
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de Minas Gerais
KeywordsComputer science

Abstract

fetched live from OpenAlex

Due to the increase of the deficit of Brazilian social housing in the last years, many actions from public politics were conducted by the Federal, State and municipal governments, as the "My Home My Life" (MCMV) program, considered a solution to reduce the deficit. In Piumhi-Brazil 434 houses were built, where several pathologies were identified in most of the buildings, what contributes to reduce the quality of the life of the inhabitants. This research aims to study the quality of the buildings and presents a proposal of an initial BIM model of a building. This proposal sets an architectonic design that reaches criteria of housing, accessibility and sustainability. 273 houses were visited in 6 different housing estates in the municipality, what enabled to evaluate the conditions of the buildings. Based on the evaluation of questionnaires an architectonic design was developed considering the basic requirements from the inhabitants, updating the proposal of the standard constructed, using the software Revit for a 3D model. It is expected that the model proposed contributes to reduce the pathologies and to increase the quality of the buildings.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.257
Teacher spread0.235 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2020
Admission routes1
Has abstractyes

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