Ecological Problems of Ukraine Related to Urbanization, Migration and State of War
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The environmental situation in Kyiv has changed as a result of the transformation of industry and its territorial structure, the role of motor vehicles. Therefore, the study of the impact of urbanization on the environmental conditions of Kyiv during the development of the post-industrial economy is topical. The aim of the research is to identify territorial specifics of environmental changes in Kyiv in the period of the post-industrial economy development in 2000-2022. General regularities and specifics of the urbanization of Kyiv and other major cities of the world in the post-industrial period were determined. The assessment of changes of sources and types of environmental pollution caused by urbanisation was conducted, which allowed estimating specifics of the post-industrial ecological conditions at a macro level, using statistical indicators of urbanisation. The methodology of the assessment of ecological usage intensity and efficiency of urbanisation reorganisation of Kyiv was developed, a comparative analysis of the urbanisation level from stationary sources and the level of ecological intensity of the use of industrial zones. Key features of dynamics and territorial structure of influence of automobile and aviation transport in Kyiv were distinguished. The methodology of complex assessment of the environmental quality change in municipal areas was applied. The practical significance of the work consists in the development of the system of ecological assessment of urbanization, which can be used to create the ecological-urban development concept of Kyiv, as well as in teaching courses and the development of practical tasks on the city ecology.
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.
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 | 0.003 | 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it