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Record W2053309597 · doi:10.4236/ijg.2012.35108

Geochemistry of Muro Banded Iron-Formation, Central Nigeria

2012· article· en· W2053309597 on OpenAlexfundno aff
John Adeyinka Adekoya, Chukwuemeka Tony Okonkwo, Mohammed Olatoye Adepoju

Bibliographic record

VenueInternational Journal of Geosciences · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsnot available
FundersMcGill University
KeywordsFaciesGeologyCarbonateSideriteGeochemistryHematiteBanded iron formationIron oxideMineralogyChloritePyrrhotiteGoethiteMagnetiteCarbonate mineralsQuartzPyritePrecambrianCalciteGeomorphologyPaleontologyStructural basinChemistry

Abstract

fetched live from OpenAlex

Muro Banded Iron-Formation occurs in the Proterozoic Toto Schist Belt, central Nigeria. It consists preponderantly of oxide facies and minor carbonate facies. The oxide facies is made up of alternating bands of quartz (metachert) with those of hematite + magnetite + martite ± goethite, chlorite, pyrrhotite and garnet. The carbonate facies consists of quartz (metachert) + siderite ± goethite. In the oxide facies the total iron content (Fe2O3t) ranges from 33.95% to 48.08% and the SiO2 content from 50.33% to 64.50%. In the case of the carbonate facies, the Fe2O3t content varies from 15.42% to 20.66% and SiO2 content from 66.84 to 72.86%. The Al2O3 content is generally low ranging from 0.1% to 0.54% in the oxide facies, and 0.24% to 0.31% in the carbonate facies. Chemically, the Muro Iron-Formation is similar to the Lake Superior-type iron-formations in terms of the distribution of the major and trace elements. This taken together with similarities in lithological associations indicates its deposition in similar environments i.e. shallow intra-continental or restricted/barred marine basin. The very low Al2O3 contents indicate minor clastic dilution of the original chemical precipitates.

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)0.000
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 score0.099
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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