Empowering Future Spectrum Management and Regulation With Large Language Models
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
Spectrum management and regulation are becoming more complex due to rapid technological advancements, increasing demand for spectrum, and the presence of diverse stakeholders with conflicting interests. In response, governments worldwide are increasingly interested in leveraging advanced technologies, such as Artificial Intelligence (AI), to enhance efficiency and optimize policy outcomes. This paper explores the application of large language models (LLMs), a subset of generative AI, to streamline tasks and improve decision-making in spectrum management and regulation. It examines the various roles of LLMs in this field and addresses associated challenges. Through empirical case studies and experimental findings, the paper demonstrates how LLMs can profoundly transform spectrum management and regulation practices. The study also offers insights into effectively integrating AI into regulatory frameworks, providing practical lessons and best practices for governmental AI initiatives.
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 | 0.000 |
| 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