Transforming Rules and Trees into Comprehensible Knowledge Structures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The problem of transforming the knowledge bases of expert systems using induced rules or decision trees into comprehensible knowledge structures is addressed. A knowledge structure is developed that generalizes and subsumes production rules, decision trees, and rules with exceptions. It gives rise to a natural complexity measure that allows them to be understood, analyzed and compared on a uniform basis. The structure is a directed acyclic graph with the semantics that nodes are premises, some of which have attached conclusions, and the arcs are inheritance links with disjunctive multiple inheritance. A detailed example is given of the generation of a range of such structures of equivalent performance for a simple problem, and the complexity measure of a particular structure is shown to relate to its perceived complexity. The simplest structures are generated by an algorithm that factors common sub-premises from the premises of rules. A more complex example of a chess dataset is used t...
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it