Context-dependent interpretations of linguistic terms in fuzzy relational databases
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
Approaches are proposed to allow fuzzy terms to be interpreted according to the context within which they are used. Such an interpretation is natural and useful. A query-dependent interpretation is proposed to allow a fuzzy term to be interpreted relative to a partial answer of a query. A scaling process is used to transform a pre-defined meaning of a fuzzy term into on appropriate meaning in the given context. Sufficient conditions are given for a nested fuzzy query with RELATIVE quantifiers to be unnested for an efficient evaluation. An attribute-dependent interpretation is proposed to model the applications in which the meaning of a fuzzy term in an attribute must be interpreted with respect to values in other related attributes. Two necessary and sufficient conditions for a tuple to have a unique attribute-dependent interpretation are provided. We describe an interpretation system that allows queries to be processed based on the attribute-dependent interpretation of the data. Two techniques, grouping and shifting, are proposed to improve the implementation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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