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Record W4224290752 · doi:10.5539/esr.v11n1p15

Petrographic and Geotechnical Characterization of Granites from the N’Goura Massif (Center-Sud/Tchad): Implication for their Used in Civil Engineering

2022· article· en· W4224290752 on OpenAlexvenueno aff
Al-hadj Hamid Zagalo, Tcheumenak Kouémo Jules, Maurice Kwékam, Allaramadji Dounia, P. Rochette

Bibliographic record

VenueEarth Science Research · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies Worldwide
Canadian institutionsnot available
Fundersnot available
KeywordsMassifPetrographyGeologyPlagioclaseFeldsparBiotiteOutcropMuscoviteQuartzChloriteAlkali feldsparGeochemistryGeotechnical engineeringMineralogy

Abstract

fetched live from OpenAlex

This work deals with the petrographic and geotechnical characterization of rocks from the N'goura massif with focus on their use in civil engineering. The study area is located in central southern Chad, about 205km to the north of N'Djamena. The N'goura massif is monzogranitic with two micas. The rocks outcrop as blocks, slabs and balls displaying fine, medium and coarse grained minerals. Monzogranite is composed of 34% quartz, 32% alkali feldspar, 26% plagioclase, 4% biotite, 2% muscovite and 1% chlorite on average. Geotechnical data show that the aggregates obtained from this rock have a Los Angeles coefficient ranging from 22.70 to 38.70% with an average of 30.70%, a Microdeval coefficient ranging from 4 to 13% with an average of 8.5% and a dynamic fragmentation coefficient ranging from 11.43 to 18.57% with an average of 15%. These results indicate that the studied materials are suitable to be used for construction and civil engineering works. The correlation between petrographic and geotechnical data reveals that the size (texture), grain structure and mineralogical composition (Qtz, Kfs and Bt+Ms+Chl+Ser) have an influence on the geotechnical behavior of these materials.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.051
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.037
GPT teacher head0.260
Teacher spread0.223 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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