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Record W4389127266 · doi:10.5151/2594-5327-14781

COMPÓSITOS DE FIBRAS DE COCO EM MATRIZ EPOXÍDICA ENSAIADAS POR IMPACTO IZOD

2009· article· gl· W4389127266 on OpenAlexaff
Lucas Lopes da Costa, Sérgio Neves Monteiro, Helvio Pessanha Guimarães Santafé Júnior

Bibliographic record

VenueABM Proceedings · 2009
Typearticle
Languagegl
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsImpact
Fundersnot available
KeywordsIzod impact strength testMaterials scienceComposite materialPhysicsUltimate tensile strength

Abstract

fetched live from OpenAlex

PDF | Fibras naturais podem aumentar consideravelmente a resistência ao impacto da matriz polimérica de compósitos. No presente trabalho investigou-se a resistência ao impacto Izod de compósitos de matriz epoxídica reforçadas com fibras de coco alinhadas de forma contínua. Corpos de prova com até 30% em volume de fibra foram produzidos por prensagem a frio em mistura com resina epóxi. Após cura, os corpos de prova entalhados foram ensaiados em pêndulo de impacto com configuração Izod. Os resultados mostraram um expressivo aumento na energia absorvida no impacto com a fração de fibras de coco. A análise por microscópio eletrônico de varredura constatou que a maior tenacidade destes compósitos é devida à fraca interface entre a fibra e a matriz epóxi. Isto provoca o descolamento ao longo da superfície das fibras, longitudinalmente à direção do impacto, acarretando maior área de fratura e conseqüente maior energia absorvida.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.259
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2009
Admission routes1
Has abstractyes

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