Wooden skyscrapers. Traditional materials in innovative technologies of modern high-rise construction
Bibliographic record
Abstract
Статья посвящена анализу актуальных материалов по внедрению инновационных технологий в проектировании небоскребов и высотном строительстве, созданных на основе древесины. Отдельные инженерно-технические разработки последних лет привели к невероятным достижениям в сфере деревянного строительства. Деревянные конструкции стали намного прочней, безопасней и экономичней, чем прочие материалы, используемые при строительстве высотных сооружений. Древесина как перспективный материал для возведения многофункциональных башен оказалась очень востребована в футуристических проектах 2000-х и 2010-х годов. Также с 2010 по 2018 год в Австрии, Канаде, Норвегии, Индии, Сингапуре и Франции было анонсировано и частично реализовано строительство нескольких высотных деревянных проектов. Причем с каждым годом наблюдается увеличение этажности и сложности предлагаемых архитектурных и конструктивных решений. The article overviews the actual publications devoted to implementation of innovative technologies based on usage of wood in skyscrapers projecting and high building construction. Some of engineering and technological inventions of the recent years led to incredible achievements in wooden construction: the wooden constructions became significantly stronger, secure and less expensive than other materials used in high building construction. In result, the orientation to implementation of various wooden constructions in the newest high building architecture has taken character of visible trend. The wood as a perspective material for creation of multifunctional towers turned out to be very essential in futuristic projects of the years of 2000 and 2010. From 2010 to 2018 too in Austria, Canada, Norway, India, Singapore and France, several “wooden” high-rise projects were announced and partly realized. And, with each new year the number of floors and complexity of the planned projects is growing.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".