RECOMMENDATIONS FOR THE IMPLEMENTATION OF THE PRINCIPLES OF ECO-CITIES IN THE URBAN AREAS OF BISHKEK
Bibliographic record
Abstract
The relevance of introducing the principles of an eco-city in the urban areas of Bishkek is due to the deterioration of the environmental performance of the city, where the main reason is the instability of the entire territory management system. The purpose of the article is to give recommendations on the implementation of the principles of the eco-city in the urban environment. Recommendations were made: 1. To improve the ecology of Bishkek, it is necessary to reduce the existing ratio of private housing (approximately 65x35) to high-rise buildings, switch to alternative energy sources, and reduce heat losses. The ratio of 65x35 actually coincides with the ratio of the stove and central heating. 2. For institutional and legislative improvement of the level, a pilot project of the "green" quarter is needed. We propose to create such a project as social housing with green infrastructure; 3. It is profitable to build a new spot development with off-grid connections, which unloads old engineering networks due to alternative energy, heat and light. With the deterioration of engineering and technical infrastructure by 90%, this initiative is relevant; 4. Living roofs, vertical gardening, green technologies are easiest to implement in commercial, public buildings; 5. The creation of a master plan for an eco-digital city remains relevant; 6. Ultra-modern "green" quarter - an investment opportunity as a point of growth.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 0.015 |
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".