PSOA Prova: PSOA Translation of Pure Production Rules to the Prova Engine.
Bibliographic record
Abstract
PSOA Prova enriches PSOA RuleML with parts of Reaction RuleML. It is implemented by combining PSOATransRun and Prova, a Prolog-based language and engine which supports object orientation, reactive programming as well as several other programming paradigms. A modified Prova engine targeted by PSOA RuleML's PSOATransRun translators is introduced and then extended to top-level assert and retract by allowing KB consult and unconsult at runtime. PSOA is further extended to pure production rules, a conclusion-asserting subset of Production RuleML, hence Reaction RuleML. This is exemplified for a PSOA Prova knowledge base about the British "Succession to the Crown Act 2013". These extensions yield a PSOA Prova language and engine available on GitHub. The PSOA Prova concepts are demonstrated with three formalization approaches for the British "Succession to the Crown Act 2013".
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 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".