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Record W4390574257 · doi:10.52155/ijpsat.v39.1.5393

Contribution A L’étude D’élaboration D’acier A Carbone A Madagascar

2023· article· fr· W4390574257 on OpenAlexaff
Richard Michel RAZANAMAHAZO, Huchard Paul Berthin RANDRIANIRAINY, Jaconnet Oliva Andrianaivoravelona

Bibliographic record

VenueInternational Journal of Progressive Sciences and Technologies · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicMetallurgy and Cultural Artifacts
Canadian institutionsCentre de Recherche Industrielle du Québec
Fundersnot available
KeywordsPhysicsHumanitiesArt

Abstract

fetched live from OpenAlex

A l’origine de sa création, c’est la recherche appliquée pour le développement qui justifie l’existence d’un organisme de recherche publique. La présente étude est une tentative de contribution à la production d’acier à Madagascar. Elle se propose une méthode qui permet de convertir les déchets métalliques en acier. Le but du travail est de valoriser les produits réfractaires siliceux du CNRIT pour le garnissage d’un petit convertisseur. Le mode d’injection par le coté latéral du convertisseur à l’aide d’une tuyère à multiple trous a permis de faire circuler un grand débit d’oxygène et d’améliorer l’oxydation des impuretés dans la fonte. Par rapport au mode de soufflage par le haut, l’injection par le coté latéral réduit la teneur en azote dans le métal. En outre, une circulation uniforme de l’oxygène sur le liquide favorise la coalescence et la remontée donc l’assimilation des particules non métalliques dans le métal par la scorie. Mais la faible capacité du four entraîne un fort accroissement de sa surface spécifique. Il y a donc une grande perte de chaleur par unité de métal.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.032
GPT teacher head0.350
Teacher spread0.318 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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