Achieving Sustainability in Smart Cities & Its Impact on Citizen
Bibliographic record
Abstract
Life became digitalized and smartly controlled that requiring more energy usage. Smart cities and urbanization focus on the challenge of worldwide urbanization through the recognition of opportunities to integrate social, physical, environmental and technological infrastructure. Urbanization expands the need of all services including water, power, transportation, as well as other facilities. All those infrastructures should be delivered to citizen within a short period of time with very well controlled systems to provide more simple and comfortable life. Furthermore, stakeholders and citizens should be responsible and cooperative with government and organizations in order to achieve better solution for smart sustainable living approach. Although the smart urbanization has the potential to become a positive transformative force for every aspect of sustainable development in cities, there is a lack of knowledge using the smart and sustainable concepts in cities. Therefore, this paper aims to propose a framework which merges the sustainable aspects with the Smart city components. The paper started by an analytical study about smart cities fields and their needs, then a study for sustainability aspects, all this end with a framework tested by a questionnaire to propose guidelines and recommendations to be followed by city planners in order to achieve sustainability goals in smart cities for better impact on citizens.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".