Author manuscript, published in "12th International Conference on Principles of Knowledge Representation and Reasoning (KR'10), Toronto: Canada (2010)" A Class of ⋄ f-consistencies for Qualitative Constraint Networks
Bibliographic record
Abstract
In this paper, we introduce a new class of local consistencies, called ⋄ f-consistencies, for qualitative constraint networks. Each consistency of this class is based on weak composition (⋄) and a mapping f that provides a covering for each relation. We study the connections existing between some properties of mappings f and the relative inference strength of ⋄ f-consistencies. The consistency obtained by the usual closure under weak composition is shown to be the weakest element of the class, and new promising perspectives are shown to be opened by ⋄ f-consistencies stronger than weak composition. We also propose a generic algorithm that allows us to compute the closure of qualitative constraint networks under any “well-behaved ” consistency of the class. The experimentation that we have conducted on qualitative constraint networks from the Interval Algebra shows the interest of these new local consistencies, in particular for the consistency problem.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.407 | 0.108 |
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".