Development and application of an integrated smart city model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study presents an innovative integrated approach for smart cities, aimed at promoting environmentally sustainable economies through novel technological and socio-economic transitions. The proposed model determines the smart city index (SCI) by aggregating 32 distinct performance indicators that significantly transform the environment, economy, energy, social, governance, and transportation sectors. This model is inherently multidisciplinary and is methodologically processed using multi-criteria decision analysis, which is aggregated using four distinct weighting schemes. The model results reveal that based on the equal weighting scheme, Sydney emerges as the city with the highest SCI score of 0.72, whereas Lima is identified as the city with the lowest SCI score of 0.26. On the other hand, based on the sustainability triad scheme, Toronto tops the list with an SCI score of 0.77, whereas Abuja scores the lowest with an SCI score of 0.31. Interestingly, Toronto, Vancouver, and Montreal continue to maintain their position among the top 5 cities across all three schemes: equal weighting, sustainability triad, and energy-focused schemes. Furthermore, the energy-focused scheme identifies Montreal as the top-performing city, scoring 0.7, followed by Oshawa at 0.67, and four Canadian cities top the SCI scores in this scheme. In contrast, Lima still remains at the bottom of the list with an SCI score of 0.27. Finally, based on a smart health-focused scheme, Sydney, Osaka, and Hämeenlinna rank highest in SCI scores. Overall, the proposed approach and model provide valuable insights and guidelines for policy-makers and urban planners to design and implement smart city initiatives that can significantly enhance sustainable development and improve quality of life in urban settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it