Conversion and integration into green infrastructure of former industrial urban quarter: theoretical model and experimental design solutions
Bibliographic record
Abstract
The regeneration of former industrial sites has become increasingly relevant in the context of urban regeneration and sustainable urban development in general. Industrial structures in urban environments, shaped by the socioeconomic conditions of their time, often fall into disuse, posing significant challenges for urban planners and developers. Such neglected sites not only deteriorate physically but also fragment urban areas, disrupting social and ecological networks. This research raises the hypothesis that by converting abandoned industrial areas into ecologically integrated urban spaces, cities can enhance public access to nature, reduce their environmental footprint, and revitalize fragmented neighborhoods. The paper includes the analysis of relevant literature on the topics of urban regeneration, building conversion, and green infrastructure, existing conversion projects, and proposes a theoretical model that guides the transformation of former industrial sites into viable, sustainable urban spaces. The formulated theoretical model was applied to experimental design of a former industrial site in Kaunas (Lithuania). The findings of the research emphasize the significance of re-establishing human interaction with nature through adaptive reuse and underline the potential social, economic, and ecological benefits of integrating formerly abandoned areas into the urban fabric.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".