Дослідження тепловитрат вузлових з’єднань конструкцій енергоефективних «зелених» будівель
Bibliographic record
Abstract
The construction industry is the main consumer of natural resources, characterized by significant consumption of non-renewable resources and the impact on environmental pollution. Up to 50% of carbon dioxide emissions are accounted for by the construction industry. In addition, the main feature of the traditional construction industry is the overuse of energy, which affects the process of global warming and climate change. Energy is consumed in the extraction of raw materials, production and transportation of materials, in the process of construction, operation, repair and liquidation of buildings. Awareness of global environmental problems has led to a rethinking of the process of design, construction, operation and disposal of buildings in the European Union, the United States, Canada and others and the emergence of the concept of sustainable development and "green" construction. In this aspect, the main criteria for designing buildings are to minimize the impact on the environment, as well as reduce energy costs, reduce waste and harmful emissions. The solution of the above problems, taking into account national specifics, can be achieved by developing typical resource- and energy-efficient, cost-effective design solutions for load-bearing and enclosing elements and their joints using environmentally friendly materials of local origin (wood and materials based on it, clay materials, straw, reeds, etc.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".