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Record W7074638209

Fixação de placas de rochas ornamentais: estudo da aderência com argamassa colante

2007· article· pt· W7074638209 on OpenAlexaboutno aff

Bibliographic record

VenueAmericanae (AECID Library) · 2007
Typearticle
Languagept
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsnot available
Fundersnot available
KeywordsThickeningFace (sociological concept)Yield (engineering)Limiting
DOInot available

Abstract

fetched live from OpenAlex

Nos revestimentos de paredes a fixação de placas de rochas pode ser feita com inserts metálicos ou por aderência com argamassas. Nos assentamentos com argamassas os valores de aderência, por norma, devem ser superiores a 1 MPa e a altura máxima do revestimento não pode ultrapassar 3 m. No presente trabalho foram feitos ensaios com ladrilhos de três tipos de \\"granitos\\" para comparar a aderência da argamassa existente no mercado, específica para estas rochas, com uma argamassa colante para porcelanatos, de mesmo custo de produção, desenvolvida por pesquisadores do Departamento de Arquitetura da Escola de Engenharia de São Carlos da Universidade de São Paulo. As rochas escolhidas foram \\"Vermelho Brasília\\" (sienogranito), \\"Verde Labrador\\" (charnoquito) e \\"Preto Indiano\\" (migmatito) apresentam características petrográficas e serrabilidades diferentes, o que implica em valores distintos de rugosidade das chapas obtidas pelo desdobramento dos blocos em teares. A aderência destas rochas com as com argamassas foi determinada, tanto na face rugosa como na face polida por meio do ensaio de arrancamento por tração, normatizado para cerâmica. Os resultados mostraram para estas rochas que a aderência das argamassas está relacionada à rugosidade e à mineralogia/textura. A aderência obtida para a argamassa colante desenvolvida para porcelanato foi aproximadamente 2 vezes superior a encontrada para argamassa comercial, mostrando sua excelente qualidade para o assentamento de placas de \\"granitos\\".

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.243
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.

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
Published2007
Admission routes1
Has abstractyes

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