Handling uncertainties of membership functions with Shadowed Fuzzy Sets
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
Type-2 fuzzy set, as an extension to ordinary fuzzy set, is defined as a fuzzy set with fuzzy membership function. Type-2 fuzzy set enables capturing the uncertainty of membership functions. However due to the three dimensional nature of type-2 fuzzy sets and the high computational complexity of their operations, in real applications, interval type-2 fuzzy sets are used. In interval type-2 fuzzy sets the distribution sitting on the top of the Footprint of Uncertainty that constitutes the third dimension of type-2 fuzzy set is ignored and hence the membership grades are intervals. This results in simpler operations and easier concept but causes loss of information. Shadowed fuzzy set discussed in this paper, provides a framework whose simplicity is comparable with interval type-2 fuzzy set but on the other hand preserves the fuzziness of the third dimension of type-2 fuzzy sets.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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