Mineralogical and Geochemical Appraisal of Clay Deposits in Papalanto and Its Environs, Southwestern, Nigeria
Bibliographic record
Abstract
Three residual clay occurrences in Papalanto, Ifo, and Imoto areas which belong to the sedimentary basin of southwestern Nigeria were investigated to determine their industrial applications.The samples were pulverized, sieved, digested with mineral acids and characterized. Clay mineralogy was determined using X-ray Diffraction (XRD). Elemental compositions of the clay samples were determined using Inductively Coupled Plasma-Mass Spectrometer (ICP-MS). Grain size distribution data were obtained by conducting grain size analysis in two parts; sieve analysis and sedimentation. Thermal properties, plasticity tests, density measurement, linear shrinkage and water absorption capacity were determined to evaluate their industrial potentials. Chemical Index of Alteration (CIA) was calculated to determine the degree of weathering in the area.The X-ray diffraction results showed that kaolinite is the dominant mineral, while quartz, anatase and hematite are the major non clay minerals. Chemical data showed that the average values of SiO2, Al2O3, and Fe2O3were 59.46%, 22.16%, and 3.06% respectively constituting 98.3% of the bulk compositions. Papalanto possessed high plasticity and mouldability.Evaluation of the clay thermal characteristics, firing colour, water absorption capacities and shrinkage values showed that the whitish Ifo clay and Papalanto kaolinitic clays could serve as raw materials for ceramics, building bricks, and other structural wares. Kaolin which is the dominant mineral in all the clays can be used for cosmetics, tooth paste, pharmaceutical purposes
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".