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Record W4245610385 · doi:10.17758/eares4.eap1118203

Mineralogical Study of Pingel-Bauchi Malachite Ore

2018· article· en· W4245610385 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and construction materials studies
Canadian institutionsnot available
FundersUniversity of Saskatchewan
KeywordsMalachiteGeologyGeochemistryMetallurgyMaterials scienceCopper

Abstract

fetched live from OpenAlex

Occurrences of over 44 economic minerals has been reported in Nigeria including copper ores found in Bauchi and Zamfara states.Now that the Nigeria petroleum industry which is the mainstay of her economy is experiencing downturn due to the global instability in oil price, it hence become imperative to diversify the economic to solid minerals particularly in the area of value addition.This research work aims at characterizing the Pingel-Bauchi malachite ore in order to determine the mineralogical assemblage, the chemical composition and the micrograph of the minerals.For this purpose, elemental analysis, chemical analysis, ore microscopy, x-ray diffractometry and scanning electron microscopy were carried out on the Pingel-Bauchi malachite ore.The result of the optical and scanning electron microscopy reveal that the Pingel-Bauchi malachite ore occur in a coarse grain locked in a porphyritic fine grain alumina and silica.It was observed that Pingel-Bauchi malachite ore contain 19.8wt% Cu which is above 0 -2.9wt%Cu which is adjudged to be the minimum copper content for economic extraction of copper ores.It was also observed that the ore contains 3.64wt% iron.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.206
Teacher spread0.194 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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