Ecological Problems of Ukraine Related to Urbanization, Migration and State of War
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".