Nature, Human and City (Appreciating the Effect of Covid-19 on Human and City)
Bibliographic record
Abstract
Despite the existing and potential characteristics of nature in urban planning process, technological progress and the rate of change in the physical identity of cities have increased natural mutations and taken the relationship between humans and nature out of the normal process. Among the natural mutations, we can mention infectious diseases, which have been nature's reaction against the city and the density of the urban population. Health issues and infectious diseases have long plagued cities, leading to changes in architecture and its rules and regulations. This process has been done less in urban planning regulations and this has increased the vulnerability of citizens in the urban environment against infectious diseases. Accordingly, this study attempted to offer principles centered on the physical nature of the city, while reviewing the history of infectious diseases in the world and considering urban planning theories related to urban health and pollution and the statistics of sample cities in the face of Covid-19 in order to accompany urban physical changes with human, technological, identity and natural changes to help urban management to reduce citizens’ vulnerability against infectious diseases. Data were collected using library and internet resources. Principles are derived from the Delphi method of experts. Some of the proposed principles are balanced building density, observing the minimum ratio of open space to urban residential space and balanced distribution of open space in the city, reducing per capita office use, establishing a crisis center with isolated conditions in each neighborhood, increasing per capita urban equipment and facilities land use, balanced distribution of neighborhood services, moving to multi-center cities, reducing concentration in city centers and using multifunctional urban spaces. Currently, due to natural mutations as well as changes in culture, traditions, and technological mutations, we need flexible rules and regulations to identify cities and align with nature. Therefore, it should be considered that the proposals offered are following the current situation and should be amended and updated over time and as circumstances change.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".