Knowledge learning and empirical research of improved pan‐logical fuzzy sets
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.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".