MétaCan
Menu
Back to cohort
Record W2017099629 · doi:10.5539/jgg.v5n2p73

Compositional, Geotechnical and Industrial Characteristics of Some Clay Bodies in Southern Nigeria

2013· article· en· W2017099629 on OpenAlexvenueno aff
TUS Onyeobi, E.G. Imeokparia, O.A. Ilegieuno, I. G. Egbuniwe

Bibliographic record

VenueJournal of Geography and Geology · 2013
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and construction materials studies
Canadian institutionsnot available
Fundersnot available
KeywordsBeneficiationKaoliniteIlliteClay mineralsGeologyShrinkageExpansive clayMineralogyGeotechnical engineeringGeochemistryMaterials scienceMetallurgyComposite materialSoil science

Abstract

fetched live from OpenAlex

Clay occurrences at Okija, Ubiaja and Iyuku in southern Nigeria were characterized geochemically, mineralogically as well as geotechnically in order to evaluate their industrial potentials. Mineralogical analyses portray kaolinite as the dominant clay mineral with traces of illite in the transported Okija and Ubiaja samples. Abundances of major elements show that SiO2 (ca 50.41-64.45%) and Al2O3 (ca 18.62-31.62%) constitute over 80% of the bulk chemical compositions. Other constituents include Fe2O3, K2O, TiO2, CaO, MgO and MnO. Although notable disparities exist in the SiO2 and Al2O3 contents of the clays, the Iyuku sample is more siliceous and less aluminous than the others. Geotechnically, the in-situ derived Iyuku clay has distinctive characteristics. It is considerably less plastic, non-expansive, less hydrophilic and of low compressibility due to its lower clay fraction and higher crystallinity of available kaolinites. On the other hand, the Okija and Ubiaja clays are characterized by medium to high plasticity and compressibility. The shrinkage characteristics of the clays as well as their colloidal activities are consistent with their plasticity. Evaluation of the industrial potential of the clays based on their physical, chemical and geotechnical characteristics revealed that they are suitable for the production of refractory bricks and ceramics. Appropriate processing/beneficiation would be mandatory if they are to qualify for other industrial applications, such as rubber, paper, paint and cosmetic industries.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.008
GPT teacher head0.189
Teacher spread0.181 · 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 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

Citations19
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Geography and GeologySame topicGeotechnical and construction materials studiesFrench-language works237,207