Lightweight Gypsum Based Materials: Methods of Preparation and Utilization
Bibliographic record
Abstract
Although gypsum is one of the most environmentally friendly building binders, its use in the buildings is relatively limited and therefore the broadening of the gypsum product portfolio is desirable.One possibility is the development of the lightweight gypsum materials with better thermal insulation properties, attractive acoustic properties and also lower transportation costs.The lightweight gypsum materials can be used in a similar way as an aerated autoclaved concrete (AAC), whose energy consumption at production is several times higher.The main methods of the preparation of gypsum-based lightweight materials are described and compared.Gypsum can be lightened indirectly by the lightweight filler or directly.In the directly lightened materials, the pores are introduced into the gypsum material either by some chemical reaction producing gas or by the help of surface active substances.For the chemical lightening a large scale of waste products can be used.Lightening by the help of waste stone powder is described in detail.The materials with the properties comparable with the properties of AAC were prepared.Their bulk density was under 600 kg/m 3 , compressive strength was about 2 MPa and coefficient of thermal conductivity was under 0.2 W/m.K.Lightweight gypsum materials can be used as a thermal insulating blocks, for the lightweight gypsum boards, partitions blocks, lightweight fire-resistant plasters or thermal-insulating plasters.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".