The production of gypsum materials with recycled gypsum-bearing components using semi-dry pressing technology
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".