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Record W4409799805 · doi:10.11159/icgre25.135

The influence of ceramic masses obtained by sintering on wall ceramics

2025· article· en· W4409799805 on OpenAlexvenueno aff
Storchay Nadiya, Nazarenko Oleksiy, Berezovskaya Alona, Klitnii Oleksandr, Zalievskyi Volodymyr

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicRecycling and utilization of industrial and municipal waste in materials production
Canadian institutionsnot available
Fundersnot available
KeywordsCeramicSinteringMaterials scienceMetallurgyComposite material

Abstract

fetched live from OpenAlex

The production of wall ceramics, despite the improvement of technology, still remains quite energy-intensive.Production in Ukraine is mainly carried out from loamy raw materials and requires high firing temperatures of 1000-1050 0 C, and, accordingly, high costs of fuel and energy resources, the cost of which is constantly growing.The existing main directions for reducing the firing temperature of wall ceramics (dispersion of raw materials, introduction of fuel-containing and other additives), when using low-quality loams, do not give the expected effect, and the firing temperature remains within the range of 950-970 0 C, and their use in combination is often constrained by the lack of a general theoretical basis for the processes of formation of the structure and physical and mechanical properties of ceramic material.Thus, reducing the firing temperature in the production of wall ceramics is an urgent problem, which may be be solved by controlling the formation of the structure and properties of ceramics obtained from activated aluminosilicate raw materials modified with Na -Fe -containing compounds.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.004
GPT teacher head0.194
Teacher spread0.189 · 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
Published2025
Admission routes1
Has abstractyes

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Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicRecycling and utilization of industrial and municipal waste in materials productionFrench-language works237,207