The Representative Curve of Type-2 Fuzzy Data Point Modeling
Bibliographic record
Abstract
This paper discusses about the construction of type-2 fuzzy data points (T2FDPs) to capture the ambiguity of complex uncertainty data points based on the type-2 fuzzy set theory (T2FST). The construction is based on the type-2 fuzzy number’s (T2FN) definition since we deal with the problem of defining the complex uncertainty data points. In order to develop T2FDPs, we use interpolating cubic Bezier curve model for better undestandingof the resultant curve. There are three procedures to obtain crisp T2FDPs in the singular data form. These procedures include fuzzification (alpha-cut operation), type-reduction and defuzzification processes. Upon carrying out these procedures, we use interpolating Bezier curve model to visualize the complex uncertainty data points denoted as type-2 fuzzy interpolation Bezier curve (T2FIBC).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".