Knowledge learning and empirical research of improved pan‐logical 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
Abstract In view of the large number of complex operations generated in the application of integrated fuzzy systems, the efficiency of system processing is affected. Based on the basis of Zadeh fuzzy sets, the study introduces the concept of opposite negation, medium negation, and contradiction in philosophical negation and computer logic. After repeated research and demonstration of the internal relations, basic characteristics, and fusion conditions between the three kinds of logical negatives, it innovatively proposed IPLF.sets. Then, the evaluation set of various logical variables and their evolution forms can be directly involved in the calculation. And then, the full membership function f ( x ) with good approximation performance is constructed by fully verifying the operation rules and the feasibility of logical transformation under the premise of the known local membership function g ( x ). The IPLF.sets practical application of the results shows that (1) dealing with complex problems can be simplified and more efficient. (2) The output is valid, reasonable, and accurate. (3) The integration of philosophical logic enhances the ability to judge the fuzzy system and improves the evaluation accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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