Development of Underground Space of Modern Cities to Improve the Condition of the Environment
Bibliographic record
Abstract
World trends in the use of underground space in the construction of modern cities were studied. It is shown that the underground space can be used for the needs of a modern city, in particular for the construction of commercial objects, trade and household institutions, entertainment, cultural and educational, sports facilities, transport networks. A well-planned and properly operated underground infrastructure improves the quality of life, energy efficiency and environmental safety to a greater extent than a similar system on the surface. At the same time, due to the placement of part of the urban infrastructure underground, the area of greenery increases and the environmental condition of large cities and their historical centers improves. Examples of the development and rational use of the underground space of cities in Canada and other countries are analyzed, the possibility of obtaining a developed network of civil defense protective structures, equipped with all the necessary communications for people’s stay, is shown, which has become especially relevant in modern war conditions. The global experience of the construction of underground transformer stations is summarized and the prospects for the implementation of the underground construction of such facilities in Ukraine are shown. The basic problems of modern urban construction include the shortage of urban areas, the accumulation of vehicles on the roads, the lack of parking spaces, the inability of the urban infrastructure to cope with the ever-increasing load, and the deterioration of the ecological situation. Changes are shown regarding the growth of energy supply to consumers from 3 to 5 kW, and the introduction of a new player on the Ukrainian electricity market - an energy storage facility operator. Placing up to 20…25 % of urban infrastructure underground allows to reduce the energy consumption of the housing stock. In the conditions of Ukraine’s refusal to import natural gas and the transition to electric heating, mainly at the expense of RES, new residential buildings will lead to a decrease in the amount of greenhouse gas emissions, and a part of the territories of cities for greening will increase, since vegetation is a significant conservator of carbon dioxide. The underground location of transformer substations in Ukraine is substantiated and proposed for the first time, which will ensure the achievement and solution of a number of problems of modern urban planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".