Consistency-based approaches to merging knowledge bases: preliminary report.
Bibliographic record
Abstract
We present a framework for investigating merging operators for belief sets. This framework is a generalisation of our earlier work concerning consistency-based belief revision and contraction. Two distinct merging operators are identified: in the first approach, belief sources are consistently combined so that the result of merging K1 and K2 is a maximal consistent (if possible) set of formulas comprising the joint knowledge of K1 and K2 . This approach then accords to one's intuitions as to what a "merge" operator should do. The second approach is more akin to a generalised belief revision operator: Two knowledge sources are "projected" onto a third (in the simplest case the trivially true knowledge base). In both cases, we consider the incorporation of entailment-based and consistency-based integrity constraints. Properties of these operators are investigated, primarily by comparing their properties with postulates that have been identified previously in the literature. As well, interrelationships between these approaches and belief revision are given.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".