Artificial intelligence for satellite communications and geophysics: current and future trends
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
In 2010, Artificial Intelligence (AI) made a breakthrough, and the technology breakthrough in the industry red line became the common expectation of the society. Driven by both market demand and national policies, the AI boom swept through China. Since the 21st century, the information superhighway has rapidly emerged, and communication technology represented by satellite communication has become increasingly important in the country's economic development. In addition, geophysics under earth sciences has also made numerous breakthroughs in theory and practice, bringing a wide range of application value for social development. There are many crossovers between the fields of communication engineering and machine learning. Geoscience has high requirements for complex and changing heterogeneous and multimodal data, and being able to analyze and process big data in combination with artificial intelligence is a direction that many scholars are exploring. This paper introduces the status of applying two technical fields of artificial intelligence in satellite communications and geophysics to explore the impact of computer technology in the research of the two fields and to look forward to the future development trend of the cooperation between the three.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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