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Record W2083256882 · doi:10.5539/jgg.v4n1p242

Radiometric Survey as a Useful Tool in Geological Mapping of Western Nigeria

2012· article· en· W2083256882 on OpenAlexvenueno aff
A. N. Amadi, N. O. Okoye, P. I. Olasehinde, I. A. Okunlola, Y. B. Alkali, T. A. Ako, J. N. Chukwu

Bibliographic record

VenueJournal of Geography and Geology · 2012
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsScintillometerGamma ray spectrometerLithologyGeologyIgneous rockImaging spectrometerSpectrometerGeochemistryRadiometric datingMineralogyRemote sensingPhysicsOpticsScintillationDetector

Abstract

fetched live from OpenAlex

Gamma ray Spectrometer (DISA-300) and broadband gamma ray scintillometer (BGS-ISL) were used to evaluate the radiometric properties of rocks (Igneous, Sedimentary and Metamorphic) in parts of southwestern Nigeria. The study revealed that although the two instruments used recorded different gamma radiation value and the graphs generated by the instruments are quite similar. Based on the major peaks and troughs of the radioactivity graph, the different formations in the area were clearly delineated and these correspond approximately to the geological boundaries in the area. Lithologic characterization of the formations revealed that the concentration of radioactive elements in rock varies. Shale, clay and granites have the highest amount of gamma count (60-105) while amphibolites show the lowest gamma count (16-46). The gamma count is a function of the concentration of radioactive elements in the rock. The reading ranges from 65-85cps in the scintillometer while the spectrometer varies from 19.75-38.88cps. The difference in readings may be attributed to the higher sensitivity of the scintillometer to gamma radiation than the spectrometer. However, the two instruments display similar pattern of curves and good correlation.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.009
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.247
Teacher spread0.222 · 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.

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

Citations11
Published2012
Admission routes1
Has abstractyes

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