[Recommendation X.904: Basic Reference Model of Open Distributed Processing - Part 4:] Architectural Semantics Amendment
Bibliographic record
Abstract
This document represents the output from the Ottawa meeting. It should be pointed out that the structure of this document is likely to change following future editing meetings. The primary focus will be on the computational viewpoint language. It is expected that a direct formalisation of this viewpoint language will be given (although not as complex as that currently existing in this document) and mapping rules to different FDTs provided. Further work on formalising other viewpoint languages is likely to take place once this work is complete. To expedite the standardising process, the other viewpoint languages will be placed in an accompanying document. Thus the formalisation of the computational viewpoint language should not be restricted from progressing to CD status by the other viewpoint language formalisations. It is likely that the document containing the formalisation of the other viewpoint languages will be recombined with the computational language formalisation document when they are more complete. 2 Scope and Field of Application
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.133 | 0.134 |
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".