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
This article analyses the concepts behind Smart Cities, and its integration with Information, Communications Technology (ITC), the Internet of Things (IoT) and Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). A smart city is a city which embraces the combination of the economy with collaboration and technology. These cities have their focus on resource efficiency and in the lenses of sustainability, it becomes a city who uses its smart resources to be environmentally amicable and reduce the carbon footprint. We will be covering an introduction to power grids, environment, transportation, and waste management systems. With the focus on Energy Management, we will further discuss the integration IoT and AI to power grids. The article addresses the main benefits of the application of AI to Energy Systems such as reduced carbon emissions from nonrenewable energy resources, energy waste prevention in homes and organizations through ML and DL, it also includes growth and infrastructure management, improved habits of consumption, and the adaptation and mitigation to climate change. This article also addresses challenges of the application of AI to Energy Management Systems, such as, ethical considerations regarding data privacy, accurate forecasting, and the decrease in funding towards green energy solutions and un updated AI educational systems.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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