The MYNG 1.01 Suite for Deliberation RuleML 1.01: Taming the Language Lattice
Bibliographic record
Abstract
harold.boley AT unb.ca Abstract. This article describes the development of MYNG to Version 1.01 in order to integrate the new Deliberation RuleML Version 1.01 Relax NG schema modules – and the RuleML sublanguages they define – into the RuleML language lattice, as well as to improve the function-ality of the MYNG GUI and REST interface. MYNG support is pro-vided for including the new modules of Deliberation RuleML 1.01 into customized schemas for sublanguages such as Datalog+, Hornlog+, and their many extensions. To also expose Disjunctive Datalog and exten-sions as RuleML sublanguages, the MYNG 1.01 GUI options are better aligned with the emergent structure of Deliberation RuleML features. To-gether, these modifications led to a vastly increased number of RuleML sublanguages in the lattice. To assist in ‘taming ’ this growth, we intro-duce the anchor lattice as an abstraction mechanism: a sublattice of the RuleML language lattice containing the most significant Deliberation RuleML sublanguages. A MYNG algorithm and interface to discover an-chors are offered. For each anchor, the highly modular Relax NG schema has been automatically converted into a monolithic XSD schema, maxi-mizing compatibility with XML tools. 1
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.020 |
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".