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Record W2949365494 · doi:10.5539/jmsr.v8n2p49

Splitting Tensile Strength, Physical and Durability Properties of Cement Stabilized Earth Block Reinforced with Treated and Untreated Pineapple Leaf Fibre

2019· article· en· W2949365494 on OpenAlexvenueno aff
Nounagnon A. Vodounon, Christopher Kanali, John Mwero, Mahfouz O. A. Djima

Bibliographic record

VenueJournal of Materials Science Research · 2019
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceUltimate tensile strengthComposite materialAbrasion (mechanical)DurabilityAbsorption of waterBlock (permutation group theory)CementMathematics

Abstract

fetched live from OpenAlex

In the present study, the physical and mechanical strength of cement stabilized earth block reinforced with treated and untreated pineapple leaf fibre (T-PALF, N-PALF) have been studied. Three types of blocks were casted, firstly the block of dimensions 290*140*120mm were casted and these block were casted for abrasion test, secondly the cube blocks of dimensions 150*150*150mm were made and they were casted for water absorption and density test; thirdly were casted cylinder block of dimensions 200*100mm for Splitting tensile strength. It was found that, the water absorption of the blocks increase with increase of fibre content, the density decrease with fibre content. The fibre have increased the abrasion resistance and tensile strength of the blocks up to 3% of fibre content afterward it decreased. It was also observed that, the T-PALF had significantly improved the properties of the blocks comparing to those reinforced with N-PALF.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.297
Teacher spread0.266 · 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 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

Citations4
Published2019
Admission routes1
Has abstractyes

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