Internet of Spacecraft for Multi-Planetary Defense and Prosperity
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
Recent years have seen unprecedentedly fast-growing prosperity in the commercial space industry. Several privately funded aerospace manufacturers, such as Space Exploration Technologies Corporation (SpaceX) and Blue Origin have transformed what we used to know about this capital-intense industry and gradually reshaped the future of human civilization. As private spaceflight and multi-planetary immigration gradually become realities from science fiction (sci-fi) and theory, both opportunities and challenges will be presented. In this article, we first review the progress in space exploration and the underlying space technologies. Next, we revisit the K-Pg extinction event and the Chelyabinsk event and predict extra-terrestrialization, terraformation, and planetary defense, including the emerging near-Earth object (NEO) observation and NEO impact avoidance technologies and strategies. Furthermore, a framework for the Solar Communication and Defense Networks (SCADN) with advanced algorithms and high efficacy is proposed to enable an Internet of distributed deep-space sensing, communications, and defense to cope with disastrous incidents such as asteroid/comet impacts. Furthermore, perspectives on the legislation, management, and supervision of founding the proposed SCADN are also discussed in depth.
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) | 0.001 | 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