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Record W4360835593 · doi:10.1016/j.rinma.2023.100394

Weibull modulus of a novel mixture of natural hydroxyapatite materials produced from biowastes

2023· article· en· W4360835593 on OpenAlexfundno aff
Obinna Anayo Osuchukwu, Abdu Salihi, Ibrahim Abdullahi, Precious Osayamen Etinosa, David O. Obada

Bibliographic record

VenueResults in Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsnot available
FundersDepartment of Mechanical Engineering, University of AlbertaAhmadu Bello University
KeywordsWeibull distributionWeibull modulusMaterials sciencePorosityComposite materialModulusCompressive strengthElastic modulusMineralogyFlexural strengthMathematicsChemistryStatistics

Abstract

fetched live from OpenAlex

In this study, the Weibull distribution of five (5) hydroxyapatite (HAp) samples produced from a novel mixture of naturally derived biowastes using the sol-gel method is reported. Compressive strength values were used to estimate the Weibull parameters. The structural, morphological, and mechanical properties of the as-produced HAp materials were investigated. The results revealed the typical hexagonal crystal structure of HAp materials. Data obtained from the Weibull analysis are comparable to the data in the literature and show how an increase in porosity is related to the Weibull modulus (m). The C100 sample showed the highest Weibull modulus.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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 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

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

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