Smartphone-based Architecture for Smart Cities
Bibliographic record
Abstract
Urban areas now account for more than half of the world's population, with the United Nations (UN) forecasting that by 2050 this number is expected to rise up to 70 percent as a result of urban growth and migration from rural areas. Increasing population density in urban environments demands adequate provision of services and infrastructure, In addition to other major challenges including air pollution, traffic congestion, health concerns, energy and waste management. Smart cities promise to leverage advances in information and communication technology to improve the quality of life for their residents in a number of ways. There are numerous applications for smart cities but the costs associated with some of the sensors used are limiting the growth of smart city technologies. Today smartphones include powerful sensors which can be accessed through customizable applications. This paper present an architecture for using sensory data generated by smartphones in order to improve the public transportation systems. The architecture provide transit officials a real time view of user demand for their systems, accounting capabilities for each bus stop, and unparalleled planning capability for additional bus routes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".