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Record W2565350806 · doi:10.5539/esr.v6n1p63

Geochemistry of Weathered Profiles over Syenite and Younger Granite in Pankshin Area, North Central Nigeria

2016· article· en· W2565350806 on OpenAlexvenueno aff
Anthony Temidayo Bolarinwa, Adewole John Adeola

Bibliographic record

VenueEarth Science Research · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMicroclineGeologyGeochemistryPlagioclaseHornblendeNepheline syeniteBiotiteNephelineQuartzWeatheringQuartz monzoniteFeldsparIlmeniteAlkali feldsparSchistPlutonMetamorphic rock

Abstract

fetched live from OpenAlex

Metallic ores of economic values often occur within profiles above basement rocks in tropical regions due to weathering and secondary enrichment. This study is carried out to determine the geochemistry and appraise the potential for metallic ore enrichment in the weathered profiles over Syenite and Younger Granite in Pankshin area. Top-soil, laterite and rock samples are collected from two lateritic profiles above Syenite and Younger Granite. The syenite rock is composed of alkali feldspars (perthite), nepheline, clinopyroxene, with minor amount of orthopyroxene, hornblende, plagioclase and few quartz grains, indicating oversaturation. The Younger Granite contains plagioclase feldspars, microcline, quartz, biotite, hornblende and reibeckite. Quartz (61.0%), kaolinite (32.0%) and microcline (7.0%) are the dominant minerals in the X-ray diffraction (XRD) of the decomposed rocks. The Chemical Index of Alteration (CIA) of both rocks is generally >90. There are enhancements of V, Sc, Zr and TiO2 in the Syenite profile and Fe2O3, Zr, V and TiO2 in the Younger Granite profile as reflected in the Accumulation Factor (AF), loss and gain of elements (K), though the values were too low for ore mineralization except for iron and titanium in the Younger Granite profile.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.272
Teacher spread0.244 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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