FEATURES OF URBAN REVITALIZATION RIVER TERRITORIES
Bibliographic record
Abstract
The article analyzes the problems of urban development and revitalization of riverside areas. It is proposed to expand the conceptual and terminological base of water legislation with the additional use of concepts from other fields of knowledge: water urbanism; water territories (land of the water fund); hydropower; coastal areas; "contact zone" of riparian coastal water territories; riverside recreation system; riverside tourism system. Complex revitalization of river valleys with a high level of urbanization involves an ecosystem approach, among which modern urban planning solutions of spatial development are leading. This becomes especially relevant against the background of climatic changes, which are gaining catastrophic dynamics in urbanized areas. This problem is highlighted in a number of international documents dedicated to the management of riverine territories and climate change problems: the Stockholm Declaration on the Environment (1972), the World Charter for Nature (1982), the Aalborg Charter "European Cities on the Road to Sustainable Development" (1994), 40th IFLA Congress on "Development of Aquatic and Coastal Ecosystems" in Calgary (2003); the 41st Congress in Taiwan (2004), as well as the "Landscape in a Changing World" program (2010), the UN Framework Convention on Climate Change, UNFCCC Paris (2015), "Habitat III Declarations" (2017 ), the sustainable development program "Rhine 2020", the UN Report "On the global development of water resources: Water and climate change" (2020).
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".