Tests of Statistical Hypotheses with Respect to a Fuzzy Set
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
Tests of statistical hypotheses with crisp data using small samples are extended to with membership function of the fuzzy sets. The t-test statistic and the F-test statistic with respect to fuzzy sets are defined using the membership grades of the fuzzy sets. The rules for taking decision about the hypotheses are provided. In the proposed tests, the optimistic and pessimistic approach, h-level set, alpha-cut and fuzzy interval are not used. Numerical examples are provided for understanding the proposed testing procedures. The proposed tests of hypotheses may be useful to decision makers who are handling real life problems involving linguistic variables / fuzzy sets for taking suitable decisions in an acceptable manner.
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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