The Impact of Database on Geographical Information System and Smart Cities
Bibliographic record
Abstract
The research discusses the substantial influence of databases in smart cities, affecting various facets and people's lives. It emphasizes the impact on Information Technology (IT) spending and database features, allowing access from anywhere at any time. Key elements crucial for smart city implementation include sustainability, adaptability, governance, improved living conditions, resource management, and city amenities. The research highlights defined components of smart cities and their applications, such as healthcare, mass transit, education, and energy services. It also focuses on how integrating Geographic Information Systems (GIS) aids decision-making. The study emphasizes the use of cloud computing for smart city database systems, outlining its benefits in data collection, storage, and analysis across various cloud nodes and facilities. It also highlights industries such as health insurance, public transportation, smart buildings, and energy that benefit from these technologies in decision-making processes. The paper's objective is to review database applications in smart cities and explore the potential use of big data..
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".