European Thermal Insulation Technology Implementation to Green Building Concept
Bibliographic record
Abstract
The amount of , electricity and other energy resources consumption is directly dependent on thermal insulation of the building. When calculating the cost for thermal insulation it should not be forgotten that the service life of the material is at least 20 years, and this procedure can reduce cost for heating by 50-80%. Up to 40% of heat is lost through the walls. The only possible way to reduce heat loss through the exterior envelope is the wall insulation. Heat loss problem applies not only to the harsh climatic conditions of Russia but also European countries with the moderately monsoon climate. Based on the data obtained under the program of international internship «Master Degree in Innovative Technologies in Energy Efficient Buildings for Russian & Armenian Universities and Stakeholders» at the University of Genoa (Italy), the article discusses the possibility of using European technology of thermal insulation of external walls in order to implement the green building concept under conditions of the transition from continental to marine climate of St. Petersburg. Considered technology is actively used in North Italy, Germany and Canada. During the work, data were obtained on the heat engineering characteristics of the method, life cycle assessment of thermal insulation materials was made, and economic feasibility of measures is given.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".