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Record W4405615637 · doi:10.5267/j.esm.2024.7.003

The production of gypsum materials with recycled gypsum-bearing components using semi-dry pressing technology

2024· article· en· W4405615637 on OpenAlex
Nataliya Alfimova, Севда Пириева, Natalia Kozhukhova, Ivan Nikulin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEngineering Solid Mechanics · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsGypsumPressingMaterials scienceBearing (navigation)Production (economics)MetallurgyWaste managementEnvironmental scienceComposite materialEngineeringComputer science

Abstract

fetched live from OpenAlex

Issues of industrial waste recycling are very relevant for the entire global scientific community. The search for technological solutions that would allow the production of high-quality materials using industrial waste will not only reduce the environmental load, but also expand the raw material base for the production of gypsum materials. The study examined the possibility of improving the surface quality of molded gypsum samples by replacing the metal mold with a plastic one and introducing a surfactant into the raw mixture. As a result of the research, it was found that the use of a surfactant and a plastic mold allows to avoid defects on the surface of the samples. At the same time, the use of a plastic mold, which has low adhesion to the citrogypsum-based binder, helps to reduce the amount of adhesion friction and optimize the raw mixture compaction process. This makes it possible to obtain samples with improved physical and mechanical characteristics (compressive strength increases by 30–85.5%, average density - by 1.7–2.7% and water absorption decreased by 1.7–16%) or lower consumption of binder up to 20% compared to samples prepared in metal mold.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.204
Teacher spread0.195 · 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