Reference Architecture for Space Data Systems - CCSDS 311.0-M-1
Bibliographic record
Abstract
Reference Architecture for Space Data Systems (RASDS) is intended to provide a standardized approach for description of space data system architectures and high-level designs. Within CCSDS the RASDS will be used for the following purposes: a) to establish an overall CCSDS recommended methodology for defining and developing domain-specific architectures; b) to define a common language, taxonomy, and representation so that challenges, requirements, and solutions in the area of space data systems can be readily communicated; c) to provide a kit of architects tools that domain experts may use to describe different specific complex space system architectures; d) to facilitate development of CCSDS Recommended Standards in a consistent way so that any standard can be used with other appropriate standards in a space data system; e) to provide a framework and guidelines for presenting the Recommended Standards developed by CCSDS in a systematic way so that their functionality, applicability, interrelationships, and interoperability may be clearly understood.
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.
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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.025 |
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".