ПРОЕКТУВАННЯ ЖИТЛОВИХ МАСИВІВ З ВИКОРИСТАННЯМ ГЕОІНФОРМАЦІЙНИХ ТЕХНОЛОГІЙ
Bibliographic record
Abstract
The article examines the current problem of designing housing arrays. They would solve not only the problem of resettlement, but also all related problems, including parking of personal cars, employment of residents, a sufficient number of places for children in schools and kindergartens. In other words such housing arrays would be comfortable to live in and would have necessary infrastructure. Analysis of global design trends shows that these problems are solved in the design of satellite cities or semi-autonomous suburban areas. We have identified the existing pros and cons of these different approaches to design. We have chosen a centric planning approach semi-autonomous area as the most rational and efficient in urban planning. We used the ArcGIS geographic information system and a vector map to analyze the existing territory of Kharkiv and to select the construction site and further design the location of buildings and infrastructure. In particular, the “buffer zones” were used for the further placement of schools, kindergartens and shops. The usage of the "buffer zones" made it possible to locate these institutions optimally, depending on the number of potencial citizens. Basing on the historical aspects of Kharkiv, a quarterly division and quarterly buildings were chosen for the projecting area, due to the fact that each quarter will have its own urban ecosystem. An algorithm for performing such works was developed by designing a residential area. It can be divided into certain stages. This algorithm can be applied while performing similar works not only to Kharkiv, but also to other cities of Ukraine and the world. The article demonstrated the possibilities of geographic information systems in the design of new types of residential areas with highly developed social and transport infrastructure, harmonious development, as well as attractive to stakeholders and future residents.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".