Planning of smart cities Performance improvement using big data analytics approach
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
The concept of smart city is widely favored, as it enhances the quality of urban citizens' life, involving multiple disciplines. Consequent to the complex urban networks, data processing complexity has increased significantly. Thus, it creates a crucial demand to facilitate autonomous decision-making and real-time data processing and analysis of smart cities. Therefore, in this paper we propose a smart city framework based on Big Data analytics. The proposed framework operates in three levels 1) Data generation and acquisition level, 2) Data management and processing level, and 3) Application level. Moreover, we analyzed the water consumption, traffic congestion, and air pollution data of Surrey (Canada) and Aarhus (Denmark) cities to determine the threshold values for data filtering. The analysis shows that the proposed architecture offers useful insights to the community development authorities to improve the existing smart city architecture.
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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