Bibliographic record
Abstract
The article concerns the problem of the use of the underground space in large cities. It provides the international case studies of urban planning of large cities: Helsinki, Oulu (Finland), London (Great Britain), Berlin (Germany), Paris (France), Madrid (Spain), Toronto (Canada), Shanghai (China), Doha (Qatar), and also the domestic experience of urban planning and renewal of Moscow. Investigation and analysis of the use of the underground space in large cities of the world have revealed and defined the ultimate objectives for the further research: the provision of comfortable living and working conditions for people; increasing the useful area of the urbanized territories of the city without the involvement of free plots of land; stabilization of the dimensions of the territory of the large city; improving the ecological environment of the city; landscaping of the undeveloped areas. The current problem of the large modern city (megalopolis) is the lack of free territories. One of the main methods of solution of this challenging task is the integrated use of the underground space. This space can be used for: underground transport facilities, industrial facilities, underground urban networks, consumer services enterprises, special purpose constructions, trade, spectacular and sports complexes, transport tunnels and underpasses, garages and parking areas. In view of the different conditions of building and planning, individual geological conditions, the use of experience of the specific city is not always applicable for another one. The studying of both foreign and domestic experience will allow to reveal the characteristic regularities and approaches for more careful and differentiated approach to the development of recommendations and project solutions on the development of the underground space in each certain large city.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".