Green Composites in Architecture and Building Material Science
Bibliographic record
Abstract
Currently, the topic of improvement of man`s live ability is becoming increasingly important. The notion of luxury living in the city includes social comfort, environment comfort (urban, natural landscape component). A wide range of small architectural forms of different architectural design and purpose is developed. The basic material for the production of small architectural forms is concrete. On optimal combination of negative and positive qualities, concrete is the most cost- effective material. In order to avoid increasing the price of hardscape, at their creating, it`s actual to use local raw materials and industrial waste. On their basis the modern high quality building materials are developed. To reduce prime costs of construction materials the technogenic raw materials are used. The solution of this actual problem possibly on the basis of expansion of a source of raw materials of the stone materials suitable for production of filler. Use of fine-grained concrete is perspective. This look allows to receive high-quality structure of a material, differs high technological effectiveness, allowing it is rather simple to make products as a pressing method with immediate striking, and it is easily and effectively modified by a molding method that is especially convenient for monolithic housing construction, and, besides, by means of organic-mineral modifiers, providing materials with various use of cheap local sand that allows to reduce concrete cost by 15-20% in comparison with coarse-grained concrete on rubble.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".