ТЕХНОЛОГІЯ МОДУЛЬНОГО БУДІВЕЛЬНОГО РІШЕННЯ SPEEDSTAC ДЛЯ ВІДНОВЛЕННЯ ЗРУЙНОВАНОГО ЖИТЛА В УКРАЇНІ
Bibliographic record
Abstract
Since the start of Russia's military invasion on February 24, 2022 and subsequent hostilities in Ukraine, about 125,000 residential buildings have been destroyed and damaged, including about 13,000 apartment buildings. After the end of the war, an urgent problem will be the quick provision of high-quality housing for about 1 million families who were left with their homes and apartments. Destroyed and damaged buildings require a systematic approach to their restoration or construction of new buildings. The article analyzes the typical destruction of high-rise prefabricated reinforced concrete residential buildings. The Speedstac modular construction solution technology from the Canadian architectural firm WZMH Architects and its research laboratory Sparkbird is proposed for the restoration of damaged and partially destroyed multi-story buildings. An example of the application of this technology for the restoration of one of the districts of Pivdenniy Saltivka in the city of Kharkiv is given.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".