Research and application of wood-concrete in world practice: an overview
Bibliographic record
Abstract
Wood-concrete products are used for the construction of exterior walls and partitions, as well as heat and sound insulation material in buildings for various purposes. They were spread in such countries as Austria, Australia, Belarus, Brazil, Canada, China, Great Britain, Germany, Holland, India, Japan, Switzerland, Russia, USA, France, etc. Wood con-crete products have high strength, low thermal conductivity, high heat capacity, are not subject to rotting, fungal and microorganism damage, and are environmentally friendly. In Polotsk State University (Belarus), a new generation of wood-concrete has been developed. The technology will make it possible to obtain a material with directional filler placement and desired properties. Arbel modifier additive allows to reduce the operating humidity and thermal conduc-tivity of the material. The method of selection of additives allows you to quickly select the composition of the additive and wood-concrete.= Изделия из деревобетона используются для возведения наружных стен и перегородок, а также в качестве тепло- и звукоизоляционного материала в зданиях различного назначения. Они получили распространение в таких странах, как Австрия, Австралия, Беларусь, Бразилия, Канада, Китай, Великобритания, Германия, Голландия, Индия, Япония, Швейцария, Россия, США, Франция и др. Изделия из деревобетона обладают высокой прочностью, низкой теплопроводностью, высокой теплоемкостью, не подвержены гниению, поражению грибками и микроорганизмами, экологически чистые. В Полоцком государственном университете (Беларусь) было разработано новое поколение деревобетона. Технология позволяет получать материал с направленным расположением заполнителя и заданными свойствами. Добавка-модификатор Арбел позволяет снизить эксплуатационную влажность и теплопроводность материала. Методика подбора добавок позволяет быстро подобрать состав добавки и деревобетона.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".