A Review on Importance of Smart City Indexes for Citizens’ Health Case Study: Esfahan City
Bibliographic record
Abstract
Smart city has been considered by urban developers and municipalities as an innovative concept in urban developing and as a strategy to contribute to the mitigation of urban problems. Smartening of cities may result in mental health and alleviation of citizens’ stress, improvement of life quality, upholding a city and citizens’ hygiene, decrement of commutations and preservation of resources. The prompt growth and sprawl of urbanization followed by high degrees of pollutions and negligence of environment have consequences for urbanites’ health, but IT is able to resolve most of those issues. The goal of present inquiry is compiling and scaling the significance level of indexes attributed to the smart city in elevation of Esfahan city health. In this regard, the researcher has exploited 22 indexes attributed to health from smart city indexes regarding practitioners’ viewpoints and elucidated the significance of which in achieving smart health through Analytic Network Process (ANP) method, the results depict that focusing on smartening in visions, plans, and initiatives of public and private organizations, considering skilled practitioners as well as designation of budgetary and funding to attain the smart health are of great importance.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".