Bibliographic record
Abstract
Active natural and artificial dynamic changes in surrounding environment raise a series of questions about the necessity of a more effective development of pre-existing urban structures of historical cities. Transform a city and make it less vulnerable to extreme climate change conditions in terms of architecture and urban design - the resilience seems only one possible answer to this kind of task. Despite the fact that the concept of resilience received light in ecology in the late 1970s thanks to Canadian ecologist (C.S.Holling), anyway until now it seems difficult to applying also in architecture and urban design studies, thereby gaining more attention in architectural scientific circles. This study aims to examine possible options of transformation existing urban morphology of historical cities in resilient systems thanks to contemporary design and planning. The study starts from the meaning of the resilient concept in general and traces the possible ways of using and applying resilient science in architecture and town planning. Then from the existing world theoretical and practical experience the study identifying the most potential resilient architectural and urban planning solutions, that allows using contemporary design as the tool of transformation existing historical fabrics in resilient structures, adapting city’s’ morphology to be ready for irreversible climate changes. The result of the research will allow adapting and using the resilient science in architecture and urbanism, as one of the tools of transformation existing historical, and not only, structures of a city to dynamic resilient structures under extreme climate change conditions.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".