LOCAL ECONETWORK of VINNYTSIA CITY
Bibliographic record
Abstract
The purpose of the study is to identify the peculiarities of the local econetwork of Vinnytsia for the sustainable urban development. Methods: field research (key, area and route), literary-cartographic, analytical-cartographic analysis, collecting and processing of statistical information, theoretical generalization and systematization of facts, analysis, abstraction, analogy, synthesis. Results. 30 key territories, which occupy 15.9% of the city area, are distinguished in the structure of Vinnytsia econetwork. According to the peculiarities of vegetation and modern landscapes, all key territories are grouped into the following groups: forest, forest-meadow, forest-swamp, pond, garden-park and cemetery. Forest key territories that correspond to the background landscapes in the past are predominant (81.7% of the area). Key territories are joined by two national and twenty five local ecological corridors, which cover 12% of the city area. River-valley ecocorridors prevail among them in area and length while street-road ecological corridors prevail in their quantity. Buffer areas, that cover 4.8% of the city’s territory, are designed around key territories and ecological corridors. Thirteen recovery territories, which occupy 0.9% of Vinnytsia area, are potential for increasing the area of key territories and ecocorridors in the future. In perspective, seven interactive elements, projected mainly by stream valleys, occupying 0.6% of the city territory, may pass into the category of ecocorridors. The novelty of the study is that for the first time: since the change of Vinnytsia’s borders in 2015, the city econetwork project has been developed; peculiarities of interactive elements and ecotechnical junctions are identified and specificated; peculiarities of the landscape complexes of the territory were taken into account while justifying the choice of structural elements of the econetwork.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".