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Análise do desempenho físico-mecânico de compósitos cimentício com adição de partículas de madeira eucalipto

2025· article· pt· W4407038919 on OpenAlexaff
Murilo Augusto Verissimo Leite, Cristiane Inácio de Campos, Alexandre Jorge Duarte de Souza, Júlio César Molina, Maria Fernanda Felippe Silva, João Vítor Felippe Silva

Bibliographic record

VenueAmbiente Construído · 2025
Typearticle
Languagept
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials science

Abstract

fetched live from OpenAlex

Resumo A utilização de materiais compósitos produzidos com adição de matérias-primas renováveis tem conquistado ampla aceitação devido à crescente escassez de recursos naturais. Esta pesquisa teve como objetivo analisar o desempenho de compósitos produzidos com cimento e madeira com dois diferentes aglomerantes, visando avaliar a resistência à compressão aos 28 dias e a densidade aparente. Foram produzidos um total de 48 corpos de prova de cimento-madeira, distribuídos em 8 tratamentos, cada um com 6 corpos de prova. Esses tratamentos variaram em relação ao tipo de cimento Portland utilizado (CP2 e CP5), à condição das partículas de madeira de Eucalyptus grandis (pré-tratadas com água fria ou in natura) e uso do aditivo cloreto de cálcio. Foram realizados o ensaio mecânico de compressão para a determinação do módulo de elasticidade secante (Ecs,28) e da resistência média do concreto à compressão aos 28 dias (fcm) e da densidade aparente dos compósitos. Os tratamentos com cimento Portland CP5 e com aditivos tiveram melhores desempenho mecânico.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.011
GPT teacher head0.276
Teacher spread0.264 · 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 designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

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