Space Link Extension Application Program Interface for Transfer Services Summary of Concept and Rationale - CCSDS 914.1-G-1
Bibliographic record
Abstract
This Report presents a summary of supplementary information supporting the CCSDS documents that specify the Application Program Interface (API) for SLE transfer services.The SLE API provides a high level, communication technology independent interface for exchange of SLE operation invocations and returns between a SLE service user and a SLE service provider. This interface is implemented by software components and is provided to software programs supporting SLE interfaces. Therefore, SLE API Recommended Practice documents make use of software specification techniques and contain detailed definitions of software interfaces. This Report presents the concepts behind the SLE API and the rationale for the SLE API in a manner that does not require in depth familiarity with software development concepts and techniques. It has been prepared for technical domain experts, who want to understand the general concepts, but might not be interested in all details required for an implementation of the SLE API. Reading this Report might also help to better understand the SLE API Recommended Practice documents. For software developers wishing to implement the SLE API, or to use available implementations of the SLE API, this Report can serve as an initial introduction to the material provided by the SLE API Recommended Practice documents.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.153 | 0.121 |
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".