The Impact of Database on Geographical Information System and Smart Cities
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 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..
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 | 0.029 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| 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