Artificial intelligence for satellite communications and geophysics: current and future trends
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.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".