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Record W4286964198 · doi:10.5281/zenodo.5562050

CARACTERISATIONS PHYSICO-CHIMIQUES ET MINERALOGIQUES DE LARGILE DAKLAKOU UTILISEE DANS LA POTERIE EN VUE DE SON APPLICATION DANS LA FORMULATION DES AGREGATS LEGERS

2021· article· en· W4286964198 on OpenAlexaff
Dodji Zounon Moursalou Koriko, Sanonka Tchegueni Agbegnigan Degbe, Gado Tchangbedji and Patrick Drogui Koffi Fiaty

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPhilosophyHumanities

Abstract

fetched live from OpenAlex

In order to contribute to the enhancement of local materials, Aklakou clays taken from the quarry and used for pottery have been characterized from a physicochemical and mineralogical point of view. Thus, the samples taken from the quarry were subjected to chemical and mineralogical analyzes. Several qualitative and quantitative analysis techniques were used (chemical analysis, thermal analysis, X-ray diffraction and analysis by I.R. spectroscopy). These results demonstrated the richness of this quartz clay, resulting in a high proportion of silica (77.62%). This study was therefore essential before any application of this type of clay in the formulation of aggregates and pottery products or even ceramics in the broad sense. We can therefore conclude that the clays mined at the Aklakou quarry are very favorable materials for formulations of pottery products and in other fields such as the formulation of light aggregates.\n\n

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.277
Teacher spread0.255 · 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 designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicClay minerals and soil interactionsFrench-language works237,207